Combined algorithmic and GPU acceleration for ultra-fast circular conebeam backprojection

NASA Astrophysics Data System (ADS)

Brokish, Jeffrey; Sack, Paul; Bresler, Yoram

2010-04-01

In this paper, we describe the first implementation and performance of a fast O(N3logN) hierarchical backprojection algorithm for cone beam CT with a circular trajectory1,developed on a modern Graphics Processing Unit (GPU). The resulting tomographic backprojection system for 3D cone beam geometry combines speedup through algorithmic improvements provided by the hierarchical backprojection algorithm with speedup from a massively parallel hardware accelerator. For data parameters typical in diagnostic CT and using a mid-range GPU card, we report reconstruction speeds of up to 360 frames per second, and relative speedup of almost 6x compared to conventional backprojection on the same hardware. The significance of these results is twofold. First, they demonstrate that the reduction in operation counts demonstrated previously for the FHBP algorithm can be translated to a comparable run-time improvement in a massively parallel hardware implementation, while preserving stringent diagnostic image quality. Second, the dramatic speedup and throughput numbers achieved indicate the feasibility of systems based on this technology, which achieve real-time 3D reconstruction for state-of-the art diagnostic CT scanners with small footprint, high-reliability, and affordable cost.

A diagnostic algorithm for atypical spitzoid tumors: guidelines for immunohistochemical and molecular assessment.

PubMed

Cho-Vega, Jeong Hee

2016-07-01

Atypical spitzoid tumors are a morphologically diverse group of rare melanocytic lesions most frequently seen in children and young adults. As atypical spitzoid tumors bear striking resemblance to Spitz nevus and spitzoid melanomas clinically and histopathologically, it is crucial to determine its malignant potential and predict its clinical behavior. To date, many researchers have attempted to differentiate atypical spitzoid tumors from unequivocal melanomas based on morphological, immonohistochemical, and molecular diagnostic differences. A diagnostic algorithm is proposed here to assess the malignant potential of atypical spitzoid tumors by using a combination of immunohistochemical and cytogenetic/molecular tests. Together with classical morphological evaluation, this algorithm includes a set of immunohistochemistry assays (p16(Ink4a), a dual-color Ki67/MART-1, and HMB45), fluorescence in situ hybridization (FISH) with five probes (6p25, 8q24, 11q13, CEN9, and 9p21), and an array-based comparative genomic hybridization. This review discusses details of the algorithm, the rationale of each test used in the algorithm, and utility of this algorithm in routine dermatopathology practice. This algorithmic approach will provide a comprehensive diagnostic tool that complements conventional histological criteria and will significantly contribute to improve the diagnosis and prediction of the clinical behavior of atypical spitzoid tumors.

Implementation of an Algorithm for Prosthetic Joint Infection: Deviations and Problems.

PubMed

Mühlhofer, Heinrich M L; Kanz, Karl-Georg; Pohlig, Florian; Lenze, Ulrich; Lenze, Florian; Toepfer, Andreas; von Eisenhart-Rothe, Ruediger; Schauwecker, Johannes

The outcome of revision surgery in arthroplasty is based on a precise diagnosis. In addition, the treatment varies based on whether the prosthetic failure is caused by aseptic or septic loosening. Algorithms can help to identify periprosthetic joint infections (PJI) and standardize diagnostic steps, however, algorithms tend to oversimplify the treatment of complex cases. We conducted a process analysis during the implementation of a PJI algorithm to determine problems and deviations associated with the implementation of this algorithm. Fifty patients who were treated after implementing a standardized algorithm were monitored retrospectively. Their treatment plans and diagnostic cascades were analyzed for deviations from the implemented algorithm. Each diagnostic procedure was recorded, compared with the algorithm, and evaluated statistically. We detected 52 deviations while treating 50 patients. In 25 cases, no discrepancy was observed. Synovial fluid aspiration was not performed in 31.8% of patients (95% confidence interval [CI], 18.1%-45.6%), while white blood cell counts (WBCs) and neutrophil differentiation were assessed in 54.5% of patients (95% CI, 39.8%-69.3%). We also observed that the prolonged incubation of cultures was not requested in 13.6% of patients (95% CI, 3.5%-23.8%). In seven of 13 cases (63.6%; 95% CI, 35.2%-92.1%), arthroscopic biopsy was performed; 6 arthroscopies were performed in discordance with the algorithm (12%; 95% CI, 3%-21%). Self-critical analysis of diagnostic processes and monitoring of deviations using algorithms are important and could increase the quality of treatment by revealing recurring faults.

A Comparative Analysis of the ADOS-G and ADOS-2 Algorithms: Preliminary Findings.

PubMed

Dorlack, Taylor P; Myers, Orrin B; Kodituwakku, Piyadasa W

2018-06-01

The Autism Diagnostic Observation Schedule (ADOS) is a widely utilized observational assessment tool for diagnosis of autism spectrum disorders. The original ADOS was succeeded by the ADOS-G with noted improvements. More recently, the ADOS-2 was introduced to further increase its diagnostic accuracy. Studies examining the validity of the ADOS have produced mixed findings, and pooled relationship trends between the algorithm versions are yet to be analyzed. The current review seeks to compare the relative merits of the ADOS-G and ADOS-2 algorithms, Modules 1-3. Eight studies met inclusion criteria for the review, and six were selected for paired comparisons of the sensitivity and specificity of the ADOS. Results indicate several contradictory findings, underscoring the importance of further study.

The Autism Diagnostic Observation Schedule, Module 4: Application of the Revised Algorithms in an Independent, Well-Defined, Dutch Sample (n = 93).

PubMed

de Bildt, Annelies; Sytema, Sjoerd; Meffert, Harma; Bastiaansen, Jojanneke A C J

2016-01-01

This study examined the discriminative ability of the revised Autism Diagnostic Observation Schedule module 4 algorithm (Hus and Lord in J Autism Dev Disord 44(8):1996-2012, 2014) in 93 Dutch males with Autism Spectrum Disorder (ASD), schizophrenia, psychopathy or controls. Discriminative ability of the revised algorithm ASD cut-off resembled the original algorithm ASD cut-off: highly specific for psychopathy and controls, lower sensitivity than Hus and Lord (2014; i.e. ASD .61, AD .53). The revised algorithm AD cut-off improved sensitivity over the original algorithm. Discriminating ASD from schizophrenia was still challenging, but the better-balanced sensitivity (.53) and specificity (.78) of the revised algorithm AD cut-off may aide clinicians' differential diagnosis. Findings support using the revised algorithm, being conceptually conform the other modules, thus improving comparability across the lifespan.

N-terminal pro-B-type natriuretic peptide diagnostic algorithm versus American Heart Association algorithm for Kawasaki disease.

PubMed

Dionne, Audrey; Meloche-Dumas, Léamarie; Desjardins, Laurent; Turgeon, Jean; Saint-Cyr, Claire; Autmizguine, Julie; Spigelblatt, Linda; Fournier, Anne; Dahdah, Nagib

2017-03-01

Diagnosis of Kawasaki disease (KD) can be challenging in the absence of a confirmatory test or pathognomonic finding, especially when clinical criteria are incomplete. We recently proposed serum N-terminal pro-B-type natriuretic peptide (NT-proBNP) as an adjunctive diagnostic test. We retrospectively tested a new algorithm to help KD diagnosis based on NT-proBNP, coronary artery dilation (CAD) at onset, and abnormal serum albumin or C-reactive protein (CRP). The goal was to assess the performance of the algorithm and compare its performance with that of the 2004 American Heart Association (AHA)/American Academy of Pediatrics (AAP) algorithm. The algorithm was tested on 124 KD patients with NT-proBNP measured on admission at the present institutions between 2007 and 2013. Age at diagnosis was 3.4 ± 3.0 years, with a median of five diagnostic criteria; and 55 of the 124 patients (44%) had incomplete KD. CA complications occurred in 64 (52%), with aneurysm in 14 (11%). Using this algorithm, 120/124 (97%) were to be treated, based on high NT-proBNP alone for 79 (64%); on onset CAD for 14 (11%); and on high CRP or low albumin for 27 (22%). Using the AHA/AAP algorithm, 22/47 (47%) of the eligible patients with incomplete KD would not have been referred for treatment, compared with 3/55 (5%) with the NT-proBNP algorithm (P < 0.001). This NT-proBNP-based algorithm is efficient to identify and treat patients with KD, including those with incomplete KD. This study paves the way for a prospective validation trial of the algorithm. © 2016 Japan Pediatric Society.

Economic evaluation of the one-hour rule-out and rule-in algorithm for acute myocardial infarction using the high-sensitivity cardiac troponin T assay in the emergency department.

PubMed

Ambavane, Apoorva; Lindahl, Bertil; Giannitsis, Evangelos; Roiz, Julie; Mendivil, Joan; Frankenstein, Lutz; Body, Richard; Christ, Michael; Bingisser, Roland; Alquezar, Aitor; Mueller, Christian

2017-01-01

The 1-hour (h) algorithm triages patients presenting with suspected acute myocardial infarction (AMI) to the emergency department (ED) towards "rule-out," "rule-in," or "observation," depending on baseline and 1-h levels of high-sensitivity cardiac troponin (hs-cTn). The economic consequences of applying the accelerated 1-h algorithm are unknown. We performed a post-hoc economic analysis in a large, diagnostic, multicenter study of hs-cTnT using central adjudication of the final diagnosis by two independent cardiologists. Length of stay (LoS), resource utilization (RU), and predicted diagnostic accuracy of the 1-h algorithm compared to standard of care (SoC) in the ED were estimated. The ED LoS, RU, and accuracy of the 1-h algorithm was compared to that achieved by the SoC at ED discharge. Expert opinion was sought to characterize clinical implementation of the 1-h algorithm, which required blood draws at ED presentation and 1h, after which "rule-in" patients were transferred for coronary angiography, "rule-out" patients underwent outpatient stress testing, and "observation" patients received SoC. Unit costs were for the United Kingdom, Switzerland, and Germany. The sensitivity and specificity for the 1-h algorithm were 87% and 96%, respectively, compared to 69% and 98% for SoC. The mean ED LoS for the 1-h algorithm was 4.3h-it was 6.5h for SoC, which is a reduction of 33%. The 1-h algorithm was associated with reductions in RU, driven largely by the shorter LoS in the ED for patients with a diagnosis other than AMI. The estimated total costs per patient were £2,480 for the 1-h algorithm compared to £4,561 for SoC, a reduction of up to 46%. The analysis shows that the use of 1-h algorithm is associated with reduction in overall AMI diagnostic costs, provided it is carefully implemented in clinical practice. These results need to be prospectively validated in the future.

Diagnostic Abilities of Variable and Enhanced Corneal Compensation Algorithms of GDx in Different Severities of Glaucoma.

PubMed

Yadav, Ravi K; Begum, Viquar U; Addepalli, Uday K; Senthil, Sirisha; Garudadri, Chandra S; Rao, Harsha L

2016-02-01

To compare the abilities of retinal nerve fiber layer (RNFL) parameters of variable corneal compensation (VCC) and enhanced corneal compensation (ECC) algorithms of scanning laser polarimetry (GDx) in detecting various severities of glaucoma. Two hundred and eighty-five eyes of 194 subjects from the Longitudinal Glaucoma Evaluation Study who underwent GDx VCC and ECC imaging were evaluated. Abilities of RNFL parameters of GDx VCC and ECC to diagnose glaucoma were compared using area under receiver operating characteristic curves (AUC), sensitivities at fixed specificities, and likelihood ratios. After excluding 5 eyes that failed to satisfy manufacturer-recommended quality parameters with ECC and 68 with VCC, 56 eyes of 41 normal subjects and 161 eyes of 121 glaucoma patients [36 eyes with preperimetric glaucoma, 52 eyes with early (MD>-6 dB), 34 with moderate (MD between -6 and -12 dB), and 39 with severe glaucoma (MD<-12 dB)] were included for the analysis. Inferior RNFL, average RNFL, and nerve fiber indicator parameters showed the best AUCs and sensitivities both with GDx VCC and ECC in diagnosing all severities of glaucoma. AUCs and sensitivities of all RNFL parameters were comparable between the VCC and ECC algorithms (P>0.20 for all comparisons). Likelihood ratios associated with the diagnostic categorization of RNFL parameters were comparable between the VCC and ECC algorithms. In scans satisfying the manufacturer-recommended quality parameters, which were significantly greater with ECC than VCC algorithm, diagnostic abilities of GDx ECC and VCC in glaucoma were similar.

Influenza detection and prediction algorithms: comparative accuracy trial in Östergötland county, Sweden, 2008-2012.

PubMed

Spreco, A; Eriksson, O; Dahlström, Ö; Timpka, T

2017-07-01

Methods for the detection of influenza epidemics and prediction of their progress have seldom been comparatively evaluated using prospective designs. This study aimed to perform a prospective comparative trial of algorithms for the detection and prediction of increased local influenza activity. Data on clinical influenza diagnoses recorded by physicians and syndromic data from a telenursing service were used. Five detection and three prediction algorithms previously evaluated in public health settings were calibrated and then evaluated over 3 years. When applied on diagnostic data, only detection using the Serfling regression method and prediction using the non-adaptive log-linear regression method showed acceptable performances during winter influenza seasons. For the syndromic data, none of the detection algorithms displayed a satisfactory performance, while non-adaptive log-linear regression was the best performing prediction method. We conclude that evidence was found for that available algorithms for influenza detection and prediction display satisfactory performance when applied on local diagnostic data during winter influenza seasons. When applied on local syndromic data, the evaluated algorithms did not display consistent performance. Further evaluations and research on combination of methods of these types in public health information infrastructures for 'nowcasting' (integrated detection and prediction) of influenza activity are warranted.

Diagnostic accuracy of administrative data algorithms in the diagnosis of osteoarthritis: a systematic review.

PubMed

Shrestha, Swastina; Dave, Amish J; Losina, Elena; Katz, Jeffrey N

2016-07-07

Administrative health care data are frequently used to study disease burden and treatment outcomes in many conditions including osteoarthritis (OA). OA is a chronic condition with significant disease burden affecting over 27 million adults in the US. There are few studies examining the performance of administrative data algorithms to diagnose OA. The purpose of this study is to perform a systematic review of administrative data algorithms for OA diagnosis; and, to evaluate the diagnostic characteristics of algorithms based on restrictiveness and reference standards. Two reviewers independently screened English-language articles published in Medline, Embase, PubMed, and Cochrane databases that used administrative data to identify OA cases. Each algorithm was classified as restrictive or less restrictive based on number and type of administrative codes required to satisfy the case definition. We recorded sensitivity and specificity of algorithms and calculated positive likelihood ratio (LR+) and positive predictive value (PPV) based on assumed OA prevalence of 0.1, 0.25, and 0.50. The search identified 7 studies that used 13 algorithms. Of these 13 algorithms, 5 were classified as restrictive and 8 as less restrictive. Restrictive algorithms had lower median sensitivity and higher median specificity compared to less restrictive algorithms when reference standards were self-report and American college of Rheumatology (ACR) criteria. The algorithms compared to reference standard of physician diagnosis had higher sensitivity and specificity than those compared to self-reported diagnosis or ACR criteria. Restrictive algorithms are more specific for OA diagnosis and can be used to identify cases when false positives have higher costs e.g. interventional studies. Less restrictive algorithms are more sensitive and suited for studies that attempt to identify all cases e.g. screening programs.

A fast Monte Carlo EM algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with AGC

PubMed Central

Kang, Le; Carter, Randy; Darcy, Kathleen; Kauderer, James; Liao, Shu-Yuan

2013-01-01

In this article we use a latent class model (LCM) with prevalence modeled as a function of covariates to assess diagnostic test accuracy in situations where the true disease status is not observed, but observations on three or more conditionally independent diagnostic tests are available. A fast Monte Carlo EM (MCEM) algorithm with binary (disease) diagnostic data is implemented to estimate parameters of interest; namely, sensitivity, specificity, and prevalence of the disease as a function of covariates. To obtain standard errors for confidence interval construction of estimated parameters, the missing information principle is applied to adjust information matrix estimates. We compare the adjusted information matrix based standard error estimates with the bootstrap standard error estimates both obtained using the fast MCEM algorithm through an extensive Monte Carlo study. Simulation demonstrates that the adjusted information matrix approach estimates the standard error similarly with the bootstrap methods under certain scenarios. The bootstrap percentile intervals have satisfactory coverage probabilities. We then apply the LCM analysis to a real data set of 122 subjects from a Gynecologic Oncology Group (GOG) study of significant cervical lesion (S-CL) diagnosis in women with atypical glandular cells of undetermined significance (AGC) to compare the diagnostic accuracy of a histology-based evaluation, a CA-IX biomarker-based test and a human papillomavirus (HPV) DNA test. PMID:24163493

Use of electronic data and existing screening tools to identify clinically significant obstructive sleep apnea.

PubMed

Severson, Carl A; Pendharkar, Sachin R; Ronksley, Paul E; Tsai, Willis H

2015-01-01

To assess the ability of electronic health data and existing screening tools to identify clinically significant obstructive sleep apnea (OSA), as defined by symptomatic or severe OSA. The present retrospective cohort study of 1041 patients referred for sleep diagnostic testing was undertaken at a tertiary sleep centre in Calgary, Alberta. A diagnosis of clinically significant OSA or an alternative sleep diagnosis was assigned to each patient through blinded independent chart review by two sleep physicians. Predictive variables were identified from online questionnaire data, and diagnostic algorithms were developed. The performance of electronically derived algorithms for identifying patients with clinically significant OSA was determined. Diagnostic performance of these algorithms was compared with versions of the STOP-Bang questionnaire and adjusted neck circumference score (ANC) derived from electronic data. Electronic questionnaire data were highly sensitive (>95%) at identifying clinically significant OSA, but not specific. Sleep diagnostic testing-determined respiratory disturbance index was very specific (specificity ≥95%) for clinically relevant disease, but not sensitive (<35%). Derived algorithms had similar accuracy to the STOP-Bang or ANC, but required fewer questions and calculations. These data suggest that a two-step process using a small number of clinical variables (maximizing sensitivity) and objective diagnostic testing (maximizing specificity) is required to identify clinically significant OSA. When used in an online setting, simple algorithms can identify clinically relevant OSA with similar performance to existing decision rules such as the STOP-Bang or ANC.

Validation of Living Donor Nephrectomy Codes

PubMed Central

Lam, Ngan N.; Lentine, Krista L.; Klarenbach, Scott; Sood, Manish M.; Kuwornu, Paul J.; Naylor, Kyla L.; Knoll, Gregory A.; Kim, S. Joseph; Young, Ann; Garg, Amit X.

2018-01-01

Background: Use of administrative data for outcomes assessment in living kidney donors is increasing given the rarity of complications and challenges with loss to follow-up. Objective: To assess the validity of living donor nephrectomy in health care administrative databases compared with the reference standard of manual chart review. Design: Retrospective cohort study. Setting: 5 major transplant centers in Ontario, Canada. Patients: Living kidney donors between 2003 and 2010. Measurements: Sensitivity and positive predictive value (PPV). Methods: Using administrative databases, we conducted a retrospective study to determine the validity of diagnostic and procedural codes for living donor nephrectomies. The reference standard was living donor nephrectomies identified through the province’s tissue and organ procurement agency, with verification by manual chart review. Operating characteristics (sensitivity and PPV) of various algorithms using diagnostic, procedural, and physician billing codes were calculated. Results: During the study period, there were a total of 1199 living donor nephrectomies. Overall, the best algorithm for identifying living kidney donors was the presence of 1 diagnostic code for kidney donor (ICD-10 Z52.4) and 1 procedural code for kidney procurement/excision (1PC58, 1PC89, 1PC91). Compared with the reference standard, this algorithm had a sensitivity of 97% and a PPV of 90%. The diagnostic and procedural codes performed better than the physician billing codes (sensitivity 60%, PPV 78%). Limitations: The donor chart review and validation study was performed in Ontario and may not be generalizable to other regions. Conclusions: An algorithm consisting of 1 diagnostic and 1 procedural code can be reliably used to conduct health services research that requires the accurate determination of living kidney donors at the population level. PMID:29662679

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

PubMed Central

Mohamed, Abdallah S. R.; Ruangskul, Manee-Naad; Awan, Musaddiq J.; Baron, Charles A.; Kalpathy-Cramer, Jayashree; Castillo, Richard; Castillo, Edward; Guerrero, Thomas M.; Kocak-Uzel, Esengul; Yang, Jinzhong; Court, Laurence E.; Kantor, Michael E.; Gunn, G. Brandon; Colen, Rivka R.; Frank, Steven J.; Garden, Adam S.; Rosenthal, David I.

2015-01-01

Purpose To develop a quality assurance (QA) workflow by using a robust, curated, manually segmented anatomic region-of-interest (ROI) library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy–simulation computed tomography (CT) with diagnostic CT coregistration. Materials and Methods Radiation therapy–simulation CT images and diagnostic CT images in 20 patients with head and neck squamous cell carcinoma treated with curative-intent intensity-modulated radiation therapy between August 2011 and May 2012 were retrospectively retrieved with institutional review board approval. Sixty-eight reference anatomic ROIs with gross tumor and nodal targets were then manually contoured on images from each examination. Diagnostic CT images were registered with simulation CT images rigidly and by using four deformable image registration (DIR) algorithms: atlas based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs by using similarity coefficient metrics (ie, Dice similarity coefficient) and surface distance metrics (ie, 95% maximum Hausdorff distance). The nonparametric Steel test with control was used to compare different DIR algorithms with rigid image registration (RIR) by using the post hoc Wilcoxon signed-rank test for stratified metric comparison. Results A total of 2720 anatomic and 50 tumor and nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for anatomic and target ROI conformance, as shown for most comparison metrics (Steel test, P < .008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures or category and simulation CT section thickness. Conclusion Development of a formal ROI-based QA workflow for registration assessment demonstrated improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck diagnostic CT and simulation CT allineation, especially for target delineation. © RSNA, 2014 Online supplemental material is available for this article. PMID:25380454

Optimal triage test characteristics to improve the cost-effectiveness of the Xpert MTB/RIF assay for TB diagnosis: a decision analysis.

PubMed

van't Hoog, Anna H; Cobelens, Frank; Vassall, Anna; van Kampen, Sanne; Dorman, Susan E; Alland, David; Ellner, Jerrold

2013-01-01

High costs are a limitation to scaling up the Xpert MTB/RIF assay (Xpert) for the diagnosis of tuberculosis in resource-constrained settings. A triaging strategy in which a sensitive but not necessarily highly specific rapid test is used to select patients for Xpert may result in a more affordable diagnostic algorithm. To inform the selection and development of particular diagnostics as a triage test we explored combinations of sensitivity, specificity and cost at which a hypothetical triage test will improve affordability of the Xpert assay. In a decision analytical model parameterized for Uganda, India and South Africa, we compared a diagnostic algorithm in which a cohort of patients with presumptive TB received Xpert to a triage algorithm whereby only those with a positive triage test were tested by Xpert. A triage test with sensitivity equal to Xpert, 75% specificity, and costs of US$5 per patient tested reduced total diagnostic costs by 42% in the Uganda setting, and by 34% and 39% respectively in the India and South Africa settings. When exploring triage algorithms with lower sensitivity, the use of an example triage test with 95% sensitivity relative to Xpert, 75% specificity and test costs $5 resulted in similar cost reduction, and was cost-effective by the WHO willingness-to-pay threshold compared to Xpert for all in Uganda, but not in India and South Africa. The gain in affordability of the examined triage algorithms increased with decreasing prevalence of tuberculosis among the cohort. A triage test strategy could potentially improve the affordability of Xpert for TB diagnosis, particularly in low-income countries and with enhanced case-finding. Tests and markers with lower accuracy than desired of a diagnostic test may fall within the ranges of sensitivity, specificity and cost required for triage tests and be developed as such.

The PHQ-8 as a measure of current depression in the general population.

PubMed

Kroenke, Kurt; Strine, Tara W; Spitzer, Robert L; Williams, Janet B W; Berry, Joyce T; Mokdad, Ali H

2009-04-01

The eight-item Patient Health Questionnaire depression scale (PHQ-8) is established as a valid diagnostic and severity measure for depressive disorders in large clinical studies. Our objectives were to assess the PHQ-8 as a depression measure in a large, epidemiological population-based study, and to determine the comparability of depression as defined by the PHQ-8 diagnostic algorithm vs. a PHQ-8 cutpoint > or = 10. Random-digit-dialed telephone survey of 198,678 participants in the 2006 Behavioral Risk Factor Surveillance Survey (BRFSS), a population-based survey in the United States. Current depression as defined by either the DSM-IV based diagnostic algorithm (i.e., major depressive or other depressive disorder) of the PHQ-8 or a PHQ-8 score > or = 10; respondent sociodemographic characteristics; number of days of impairment in the past 30 days in multiple domains of health-related quality of life (HRQoL). The prevalence of current depression was similar whether defined by the diagnostic algorithm or a PHQ-8 score > or = 10 (9.1% vs. 8.6%). Depressed patients had substantially more days of impairment across multiple domains of HRQoL, and the impairment was nearly identical in depressed groups defined by either method. Of the 17,040 respondents with a PHQ-8 score > or = 10, major depressive disorder was present in 49.7%, other depressive disorder in 23.9%, depressed mood or anhedonia in another 22.8%, and no evidence of depressive disorder or depressive symptoms in only 3.5%. The PHQ-8 diagnostic algorithm rather than an independent structured psychiatric interview was used as the criterion standard. The PHQ-8 is a useful depression measure for population-based studies, and either its diagnostic algorithm or a cutpoint > or = 10 can be used for defining current depression.

A randomized controlled trial of a diagnostic algorithm for symptoms of uncomplicated cystitis at an out-of-hours service

PubMed Central

Grude, Nils; Lindbaek, Morten

2015-01-01

Objective. To compare the clinical outcome of patients presenting with symptoms of uncomplicated cystitis who were seen by a doctor, with patients who were given treatment following a diagnostic algorithm. Design. Randomized controlled trial. Setting. Out-of-hours service, Oslo, Norway. Intervention. Women with typical symptoms of uncomplicated cystitis were included in the trial in the time period September 2010–November 2011. They were randomized into two groups. One group received standard treatment according to the diagnostic algorithm, the other group received treatment after a regular consultation by a doctor. Subjects. Women (n = 441) aged 16–55 years. Mean age in both groups 27 years. Main outcome measures. Number of days until symptomatic resolution. Results. No significant differences were found between the groups in the basic patient demographics, severity of symptoms, or percentage of urine samples with single culture growth. A median of three days until symptomatic resolution was found in both groups. By day four 79% in the algorithm group and 72% in the regular consultation group were free of symptoms (p = 0.09). The number of patients who contacted a doctor again in the follow-up period and received alternative antibiotic treatment was insignificantly higher (p = 0.08) after regular consultation than after treatment according to the diagnostic algorithm. There were no cases of severe pyelonephritis or hospital admissions during the follow-up period. Conclusion. Using a diagnostic algorithm is a safe and efficient method for treating women with symptoms of uncomplicated cystitis at an out-of-hours service. This simplification of treatment strategy can lead to a more rational use of consultation time and a stricter adherence to National Antibiotic Guidelines for a common disorder. PMID:25961367

A randomized controlled trial of a diagnostic algorithm for symptoms of uncomplicated cystitis at an out-of-hours service.

PubMed

Bollestad, Marianne; Grude, Nils; Lindbaek, Morten

2015-06-01

To compare the clinical outcome of patients presenting with symptoms of uncomplicated cystitis who were seen by a doctor, with patients who were given treatment following a diagnostic algorithm. Randomized controlled trial. Out-of-hours service, Oslo, Norway. Women with typical symptoms of uncomplicated cystitis were included in the trial in the time period September 2010-November 2011. They were randomized into two groups. One group received standard treatment according to the diagnostic algorithm, the other group received treatment after a regular consultation by a doctor. Women (n = 441) aged 16-55 years. Mean age in both groups 27 years. Number of days until symptomatic resolution. No significant differences were found between the groups in the basic patient demographics, severity of symptoms, or percentage of urine samples with single culture growth. A median of three days until symptomatic resolution was found in both groups. By day four 79% in the algorithm group and 72% in the regular consultation group were free of symptoms (p = 0.09). The number of patients who contacted a doctor again in the follow-up period and received alternative antibiotic treatment was insignificantly higher (p = 0.08) after regular consultation than after treatment according to the diagnostic algorithm. There were no cases of severe pyelonephritis or hospital admissions during the follow-up period. Using a diagnostic algorithm is a safe and efficient method for treating women with symptoms of uncomplicated cystitis at an out-of-hours service. This simplification of treatment strategy can lead to a more rational use of consultation time and a stricter adherence to National Antibiotic Guidelines for a common disorder.

Cost-effectiveness analysis of microscopic observation drug susceptibility test versus Xpert MTB/Rif test for diagnosis of pulmonary tuberculosis in HIV patients in Uganda.

PubMed

Walusimbi, Simon; Kwesiga, Brendan; Rodrigues, Rashmi; Haile, Melles; de Costa, Ayesha; Bogg, Lennart; Katamba, Achilles

2016-10-10

Microscopic Observation Drug Susceptibility (MODS) and Xpert MTB/Rif (Xpert) are highly sensitive tests for diagnosis of pulmonary tuberculosis (PTB). This study evaluated the cost effectiveness of utilizing MODS versus Xpert for diagnosis of active pulmonary TB in HIV infected patients in Uganda. A decision analysis model comparing MODS versus Xpert for TB diagnosis was used. Costs were estimated by measuring and valuing relevant resources required to perform the MODS and Xpert tests. Diagnostic accuracy data of the tests were obtained from systematic reviews involving HIV infected patients. We calculated base values for unit costs and varied several assumptions to obtain the range estimates. Cost effectiveness was expressed as costs per TB patient diagnosed for each of the two diagnostic strategies. Base case analysis was performed using the base estimates for unit cost and diagnostic accuracy of the tests. Sensitivity analysis was performed using a range of value estimates for resources, prevalence, number of tests and diagnostic accuracy. The unit cost of MODS was US$ 6.53 versus US$ 12.41 of Xpert. Consumables accounted for 59 % (US$ 3.84 of 6.53) of the unit cost for MODS and 84 % (US$10.37 of 12.41) of the unit cost for Xpert. The cost effectiveness ratio of the algorithm using MODS was US$ 34 per TB patient diagnosed compared to US$ 71 of the algorithm using Xpert. The algorithm using MODS was more cost-effective compared to the algorithm using Xpert for a wide range of different values of accuracy, cost and TB prevalence. The cost (threshold value), where the algorithm using Xpert was optimal over the algorithm using MODS was US$ 5.92. MODS versus Xpert was more cost-effective for the diagnosis of PTB among HIV patients in our setting. Efforts to scale-up MODS therefore need to be explored. However, since other non-economic factors may still favour the use of Xpert, the current cost of the Xpert cartridge still needs to be reduced further by more than half, in order to make it economically competitive with MODS.

Head-to-head comparison of adaptive statistical and model-based iterative reconstruction algorithms for submillisievert coronary CT angiography.

PubMed

Benz, Dominik C; Fuchs, Tobias A; Gräni, Christoph; Studer Bruengger, Annina A; Clerc, Olivier F; Mikulicic, Fran; Messerli, Michael; Stehli, Julia; Possner, Mathias; Pazhenkottil, Aju P; Gaemperli, Oliver; Kaufmann, Philipp A; Buechel, Ronny R

2018-02-01

Iterative reconstruction (IR) algorithms allow for a significant reduction in radiation dose of coronary computed tomography angiography (CCTA). We performed a head-to-head comparison of adaptive statistical IR (ASiR) and model-based IR (MBIR) algorithms to assess their impact on quantitative image parameters and diagnostic accuracy for submillisievert CCTA. CCTA datasets of 91 patients were reconstructed using filtered back projection (FBP), increasing contributions of ASiR (20, 40, 60, 80, and 100%), and MBIR. Signal and noise were measured in the aortic root to calculate signal-to-noise ratio (SNR). In a subgroup of 36 patients, diagnostic accuracy of ASiR 40%, ASiR 100%, and MBIR for diagnosis of coronary artery disease (CAD) was compared with invasive coronary angiography. Median radiation dose was 0.21 mSv for CCTA. While increasing levels of ASiR gradually reduced image noise compared with FBP (up to - 48%, P < 0.001), MBIR provided largest noise reduction (-79% compared with FBP) outperforming ASiR (-59% compared with ASiR 100%; P < 0.001). Increased noise and lower SNR with ASiR 40% and ASiR 100% resulted in substantially lower diagnostic accuracy to detect CAD as diagnosed by invasive coronary angiography compared with MBIR: sensitivity and specificity were 100 and 37%, 100 and 57%, and 100 and 74% for ASiR 40%, ASiR 100%, and MBIR, respectively. MBIR offers substantial noise reduction with increased SNR, paving the way for implementation of submillisievert CCTA protocols in clinical routine. In contrast, inferior noise reduction by ASiR negatively affects diagnostic accuracy of submillisievert CCTA for CAD detection. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.

Seizures in the elderly: development and validation of a diagnostic algorithm.

PubMed

Dupont, Sophie; Verny, Marc; Harston, Sandrine; Cartz-Piver, Leslie; Schück, Stéphane; Martin, Jennifer; Puisieux, François; Alecu, Cosmin; Vespignani, Hervé; Marchal, Cécile; Derambure, Philippe

2010-05-01

Seizures are frequent in the elderly, but their diagnosis can be challenging. The objective of this work was to develop and validate an expert-based algorithm for the diagnosis of seizures in elderly people. A multidisciplinary group of neurologists and geriatricians developed a diagnostic algorithm using a combination of selected clinical, electroencephalographical and radiological criteria. The algorithm was validated by multicentre retrospective analysis of data of patients referred for specific symptoms and classified by the experts as epileptic patients or not. The algorithm was applied to all the patients, and the diagnosis provided by the algorithm was compared to the clinical diagnosis of the experts. Twenty-nine clinical, electroencephalographical and radiological criteria were selected for the algorithm. According to criteria combination, seizures were classified in four levels of diagnosis: certain, highly probable, possible or improbable. To validate the algorithm, the medical records of 269 elderly patients were analyzed (138 with epileptic seizures, 131 with non-epileptic manifestations). Patients were mainly referred for a transient focal deficit (40%), confusion (38%), unconsciousness (27%). The algorithm best classified certain and probable seizures versus possible and improbable seizures, with 86.2% sensitivity and 67.2% specificity. Using logistical regression, 2 simplified models were developed, the first with 13 criteria (Se 85.5%, Sp 90.1%), and the second with 7 criteria only (Se 84.8%, Sp 88.6%). In conclusion, the present study validated the use of a revised diagnostic algorithm to help diagnosis epileptic seizures in the elderly. A prospective study is planned to further validate this algorithm. Copyright 2010 Elsevier B.V. All rights reserved.

Cost-effectiveness of novel algorithms for rapid diagnosis of tuberculosis in HIV-infected individuals in Uganda.

PubMed

Shah, Maunank; Dowdy, David; Joloba, Moses; Ssengooba, Willy; Manabe, Yukari C; Ellner, Jerrold; Dorman, Susan E

2013-11-28

Xpert MTB/RIF ('Xpert') and urinary lateral-flow lipoarabinomannan (LF-LAM) assays offer rapid tuberculosis (TB) diagnosis. This study evaluated the cost-effectiveness of novel diagnostic algorithms utilizing combinations of Xpert and LF-LAM for the detection of active TB among people living with HIV. Cost-effectiveness analysis using data from a comparative study of LF-LAM and Xpert, with a target population of HIV-infected individuals with signs/symptoms of TB in Uganda. A decision-analysis model compared multiple strategies for rapid TB diagnosis:sputum smear-microscopy; sputum Xpert; smear-microscopy combined with LF-LAM; and Xpert combined with LF-LAM. Primary outcomes were the costs and DALY's averted for each algorithm. Cost-effectiveness was represented using incremental cost-effectiveness ratios (ICER). Compared with an algorithm of Xpert testing alone, the combination of Xpert with LF-LAM was considered highly cost-effective (ICER $57/DALY-averted) at a willingness to pay threshold of Ugandan GDP per capita. Addition of urine LF-LAM testing to smear-microscopy was a less effective strategy than Xpert replacement of smear-microscopy, but was less costly and also considered highly cost-effective (ICER $33 per DALY-averted) compared with continued usage of smear-microscopy alone. Cost-effectiveness of the Xpert plus LF-LAM algorithm was most influenced by HIV/ART costs and life-expectancy of patients after TB treatment. The addition of urinary LF-LAM to TB diagnostic algorithms for HIV-infected individuals is highly cost-effective compared with usage of either sputum smear-microscopy or Xpert alone.

COMPARISON OF ADAPTIVE STATISTICAL ITERATIVE RECONSTRUCTION (ASIR™) AND MODEL-BASED ITERATIVE RECONSTRUCTION (VEO™) FOR PAEDIATRIC ABDOMINAL CT EXAMINATIONS: AN OBSERVER PERFORMANCE STUDY OF DIAGNOSTIC IMAGE QUALITY.

PubMed

Hultenmo, Maria; Caisander, Håkan; Mack, Karsten; Thilander-Klang, Anne

2016-06-01

The diagnostic image quality of 75 paediatric abdominal computed tomography (CT) examinations reconstructed with two different iterative reconstruction (IR) algorithms-adaptive statistical IR (ASiR™) and model-based IR (Veo™)-was compared. Axial and coronal images were reconstructed with 70 % ASiR with the Soft™ convolution kernel and with the Veo algorithm. The thickness of the reconstructed images was 2.5 or 5 mm depending on the scanning protocol used. Four radiologists graded the delineation of six abdominal structures and the diagnostic usefulness of the image quality. The Veo reconstruction significantly improved the visibility of most of the structures compared with ASiR in all subgroups of images. For coronal images, the Veo reconstruction resulted in significantly improved ratings of the diagnostic use of the image quality compared with the ASiR reconstruction. This was not seen for the axial images. The greatest improvement using Veo reconstruction was observed for the 2.5 mm coronal slices. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Comparison of Different Post-Processing Algorithms for Dynamic Susceptibility Contrast Perfusion Imaging of Cerebral Gliomas.

PubMed

Kudo, Kohsuke; Uwano, Ikuko; Hirai, Toshinori; Murakami, Ryuji; Nakamura, Hideo; Fujima, Noriyuki; Yamashita, Fumio; Goodwin, Jonathan; Higuchi, Satomi; Sasaki, Makoto

2017-04-10

The purpose of the present study was to compare different software algorithms for processing DSC perfusion images of cerebral tumors with respect to i) the relative CBV (rCBV) calculated, ii) the cutoff value for discriminating low- and high-grade gliomas, and iii) the diagnostic performance for differentiating these tumors. Following approval of institutional review board, informed consent was obtained from all patients. Thirty-five patients with primary glioma (grade II, 9; grade III, 8; and grade IV, 18 patients) were included. DSC perfusion imaging was performed with 3-Tesla MRI scanner. CBV maps were generated by using 11 different algorithms of four commercially available software and one academic program. rCBV of each tumor compared to normal white matter was calculated by ROI measurements. Differences in rCBV value were compared between algorithms for each tumor grade. Receiver operator characteristics analysis was conducted for the evaluation of diagnostic performance of different algorithms for differentiating between different grades. Several algorithms showed significant differences in rCBV, especially for grade IV tumors. When differentiating between low- (II) and high-grade (III/IV) tumors, the area under the ROC curve (Az) was similar (range 0.85-0.87), and there were no significant differences in Az between any pair of algorithms. In contrast, the optimal cutoff values varied between algorithms (range 4.18-6.53). rCBV values of tumor and cutoff values for discriminating low- and high-grade gliomas differed between software packages, suggesting that optimal software-specific cutoff values should be used for diagnosis of high-grade gliomas.

Retinex enhancement of infrared images.

PubMed

Li, Ying; He, Renjie; Xu, Guizhi; Hou, Changzhi; Sun, Yunyan; Guo, Lei; Rao, Liyun; Yan, Weili

2008-01-01

With the ability of imaging the temperature distribution of body, infrared imaging is promising in diagnostication and prognostication of diseases. However the poor quality of the raw original infrared images prevented applications and one of the essential problems is the low contrast appearance of the imagined object. In this paper, the image enhancement technique based on the Retinex theory is studied, which is a process that automatically retrieve the visual realism to images. The algorithms, including Frackle-McCann algorithm, McCann99 algorithm, single-scale Retinex algorithm, multi-scale Retinex algorithm and multi-scale Retinex algorithm with color restoration, are experienced to the enhancement of infrared images. The entropy measurements along with the visual inspection were compared and results shown the algorithms based on Retinex theory have the ability in enhancing the infrared image. Out of the algorithms compared, MSRCR demonstrated the best performance.

A new full-field digital mammography system with and without the use of an advanced post-processing algorithm: comparison of image quality and diagnostic performance.

PubMed

Ahn, Hye Shin; Kim, Sun Mi; Jang, Mijung; Yun, Bo La; Kim, Bohyoung; Ko, Eun Sook; Han, Boo-Kyung; Chang, Jung Min; Yi, Ann; Cho, Nariya; Moon, Woo Kyung; Choi, Hye Young

2014-01-01

To compare new full-field digital mammography (FFDM) with and without use of an advanced post-processing algorithm to improve image quality, lesion detection, diagnostic performance, and priority rank. During a 22-month period, we prospectively enrolled 100 cases of specimen FFDM mammography (Brestige®), which was performed alone or in combination with a post-processing algorithm developed by the manufacturer: group A (SMA), specimen mammography without application of "Mammogram enhancement ver. 2.0"; group B (SMB), specimen mammography with application of "Mammogram enhancement ver. 2.0". Two sets of specimen mammographies were randomly reviewed by five experienced radiologists. Image quality, lesion detection, diagnostic performance, and priority rank with regard to image preference were evaluated. Three aspects of image quality (overall quality, contrast, and noise) of the SMB were significantly superior to those of SMA (p < 0.05). SMB was significantly superior to SMA for visualizing calcifications (p < 0.05). Diagnostic performance, as evaluated by cancer score, was similar between SMA and SMB. SMB was preferred to SMA by four of the five reviewers. The post-processing algorithm may improve image quality with better image preference in FFDM than without use of the software.

Automatic analysis and classification of surface electromyography.

PubMed

Abou-Chadi, F E; Nashar, A; Saad, M

2001-01-01

In this paper, parametric modeling of surface electromyography (EMG) algorithms that facilitates automatic SEMG feature extraction and artificial neural networks (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of myopathic disorders. Three paradigms of ANN were investigated: the multilayer backpropagation algorithm, the self-organizing feature map algorithm and a probabilistic neural network model. The performance of the three classifiers was compared with that of the old Fisher linear discriminant (FLD) classifiers. The results have shown that the three ANN models give higher performance. The percentage of correct classification reaches 90%. Poorer diagnostic performance was obtained from the FLD classifier. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device.

Development of Serum Marker Models to Increase Diagnostic Accuracy of Advanced Fibrosis in Nonalcoholic Fatty Liver Disease: The New LINKI Algorithm Compared with Established Algorithms.

PubMed

Lykiardopoulos, Byron; Hagström, Hannes; Fredrikson, Mats; Ignatova, Simone; Stål, Per; Hultcrantz, Rolf; Ekstedt, Mattias; Kechagias, Stergios

2016-01-01

Detection of advanced fibrosis (F3-F4) in nonalcoholic fatty liver disease (NAFLD) is important for ascertaining prognosis. Serum markers have been proposed as alternatives to biopsy. We attempted to develop a novel algorithm for detection of advanced fibrosis based on a more efficient combination of serological markers and to compare this with established algorithms. We included 158 patients with biopsy-proven NAFLD. Of these, 38 had advanced fibrosis. The following fibrosis algorithms were calculated: NAFLD fibrosis score, BARD, NIKEI, NASH-CRN regression score, APRI, FIB-4, King´s score, GUCI, Lok index, Forns score, and ELF. Study population was randomly divided in a training and a validation group. A multiple logistic regression analysis using bootstrapping methods was applied to the training group. Among many variables analyzed age, fasting glucose, hyaluronic acid and AST were included, and a model (LINKI-1) for predicting advanced fibrosis was created. Moreover, these variables were combined with platelet count in a mathematical way exaggerating the opposing effects, and alternative models (LINKI-2) were also created. Models were compared using area under the receiver operator characteristic curves (AUROC). Of established algorithms FIB-4 and King´s score had the best diagnostic accuracy with AUROCs 0.84 and 0.83, respectively. Higher accuracy was achieved with the novel LINKI algorithms. AUROCs in the total cohort for LINKI-1 was 0.91 and for LINKI-2 models 0.89. The LINKI algorithms for detection of advanced fibrosis in NAFLD showed better accuracy than established algorithms and should be validated in further studies including larger cohorts.

Predicting Out-of-Office Blood Pressure in the Clinic for the Diagnosis of Hypertension in Primary Care: An Economic Evaluation.

PubMed

Monahan, Mark; Jowett, Sue; Lovibond, Kate; Gill, Paramjit; Godwin, Marshall; Greenfield, Sheila; Hanley, Janet; Hobbs, F D Richard; Martin, Una; Mant, Jonathan; McKinstry, Brian; Williams, Bryan; Sheppard, James P; McManus, Richard J

2018-02-01

Clinical guidelines in the United States and United Kingdom recommend that individuals with suspected hypertension should have ambulatory blood pressure (BP) monitoring to confirm the diagnosis. This approach reduces misdiagnosis because of white coat hypertension but will not identify people with masked hypertension who may benefit from treatment. The Predicting Out-of-Office Blood Pressure (PROOF-BP) algorithm predicts masked and white coat hypertension based on patient characteristics and clinic BP, improving the accuracy of diagnosis while limiting subsequent ambulatory BP monitoring. This study assessed the cost-effectiveness of using this tool in diagnosing hypertension in primary care. A Markov cost-utility cohort model was developed to compare diagnostic strategies: the PROOF-BP approach, including those with clinic BP ≥130/80 mm Hg who receive ambulatory BP monitoring as guided by the algorithm, compared with current standard diagnostic strategies including those with clinic BP ≥140/90 mm Hg combined with further monitoring (ambulatory BP monitoring as reference, clinic, and home monitoring also assessed). The model adopted a lifetime horizon with a 3-month time cycle, taking a UK Health Service/Personal Social Services perspective. The PROOF-BP algorithm was cost-effective in screening all patients with clinic BP ≥130/80 mm Hg compared with current strategies that only screen those with clinic BP ≥140/90 mm Hg, provided healthcare providers were willing to pay up to £20 000 ($26 000)/quality-adjusted life year gained. Deterministic and probabilistic sensitivity analyses supported the base-case findings. The PROOF-BP algorithm seems to be cost-effective compared with the conventional BP diagnostic options in primary care. Its use in clinical practice is likely to lead to reduced cardiovascular disease, death, and disability. © 2017 American Heart Association, Inc.

HIV misdiagnosis in sub-Saharan Africa: performance of diagnostic algorithms at six testing sites

PubMed Central

Kosack, Cara S.; Shanks, Leslie; Beelaert, Greet; Benson, Tumwesigye; Savane, Aboubacar; Ng’ang’a, Anne; Andre, Bita; Zahinda, Jean-Paul BN; Fransen, Katrien; Page, Anne-Laure

2017-01-01

Abstract Introduction: We evaluated the diagnostic accuracy of HIV testing algorithms at six programmes in five sub-Saharan African countries. Methods: In this prospective multisite diagnostic evaluation study (Conakry, Guinea; Kitgum, Uganda; Arua, Uganda; Homa Bay, Kenya; Doula, Cameroun and Baraka, Democratic Republic of Congo), samples from clients (greater than equal to five years of age) testing for HIV were collected and compared to a state-of-the-art algorithm from the AIDS reference laboratory at the Institute of Tropical Medicine, Belgium. The reference algorithm consisted of an enzyme-linked immuno-sorbent assay, a line-immunoassay, a single antigen-enzyme immunoassay and a DNA polymerase chain reaction test. Results: Between August 2011 and January 2015, over 14,000 clients were tested for HIV at 6 HIV counselling and testing sites. Of those, 2786 (median age: 30; 38.1% males) were included in the study. Sensitivity of the testing algorithms ranged from 89.5% in Arua to 100% in Douala and Conakry, while specificity ranged from 98.3% in Doula to 100% in Conakry. Overall, 24 (0.9%) clients, and as many as 8 per site (1.7%), were misdiagnosed, with 16 false-positive and 8 false-negative results. Six false-negative specimens were retested with the on-site algorithm on the same sample and were found to be positive. Conversely, 13 false-positive specimens were retested: 8 remained false-positive with the on-site algorithm. Conclusions: The performance of algorithms at several sites failed to meet expectations and thresholds set by the World Health Organization, with unacceptably high rates of false results. Alongside the careful selection of rapid diagnostic tests and the validation of algorithms, strictly observing correct procedures can reduce the risk of false results. In the meantime, to identify false-positive diagnoses at initial testing, patients should be retested upon initiating antiretroviral therapy. PMID:28691437

HIV misdiagnosis in sub-Saharan Africa: performance of diagnostic algorithms at six testing sites.

PubMed

Kosack, Cara S; Shanks, Leslie; Beelaert, Greet; Benson, Tumwesigye; Savane, Aboubacar; Ng'ang'a, Anne; Andre, Bita; Zahinda, Jean-Paul Bn; Fransen, Katrien; Page, Anne-Laure

2017-07-03

We evaluated the diagnostic accuracy of HIV testing algorithms at six programmes in five sub-Saharan African countries. In this prospective multisite diagnostic evaluation study (Conakry, Guinea; Kitgum, Uganda; Arua, Uganda; Homa Bay, Kenya; Doula, Cameroun and Baraka, Democratic Republic of Congo), samples from clients (greater than equal to five years of age) testing for HIV were collected and compared to a state-of-the-art algorithm from the AIDS reference laboratory at the Institute of Tropical Medicine, Belgium. The reference algorithm consisted of an enzyme-linked immuno-sorbent assay, a line-immunoassay, a single antigen-enzyme immunoassay and a DNA polymerase chain reaction test. Between August 2011 and January 2015, over 14,000 clients were tested for HIV at 6 HIV counselling and testing sites. Of those, 2786 (median age: 30; 38.1% males) were included in the study. Sensitivity of the testing algorithms ranged from 89.5% in Arua to 100% in Douala and Conakry, while specificity ranged from 98.3% in Doula to 100% in Conakry. Overall, 24 (0.9%) clients, and as many as 8 per site (1.7%), were misdiagnosed, with 16 false-positive and 8 false-negative results. Six false-negative specimens were retested with the on-site algorithm on the same sample and were found to be positive. Conversely, 13 false-positive specimens were retested: 8 remained false-positive with the on-site algorithm. The performance of algorithms at several sites failed to meet expectations and thresholds set by the World Health Organization, with unacceptably high rates of false results. Alongside the careful selection of rapid diagnostic tests and the validation of algorithms, strictly observing correct procedures can reduce the risk of false results. In the meantime, to identify false-positive diagnoses at initial testing, patients should be retested upon initiating antiretroviral therapy.

Improved Temperature Diagnostic for Non-Neutral Plasmas with Single-Electron Resolution

NASA Astrophysics Data System (ADS)

Shanman, Sabrina; Evans, Lenny; Fajans, Joel; Hunter, Eric; Nelson, Cheyenne; Sierra, Carlos; Wurtele, Jonathan

2016-10-01

Plasma temperature diagnostics in a Penning-Malmberg trap are essential for reliably obtaining cold, non-neutral plasmas. We have developed a setup for detecting the initial electrons that escape from a trapped pure electron plasma as the confining electrode potential is slowly reduced. The setup minimizes external noise by using a silicon photomultiplier to capture light emitted from an MCP-amplified phosphor screen. To take advantage of this enhanced resolution, we have developed a new plasma temperature diagnostic analysis procedure which takes discrete electron arrival times as input. We have run extensive simulations comparing this new discrete algorithm to our existing exponential fitting algorithm. These simulations are used to explore the behavior of these two temperature diagnostic procedures at low N and at high electronic noise. This work was supported by the DOE DE-FG02-06ER54904, and the NSF 1500538-PHY.

New web-based algorithm to improve rigid gas permeable contact lens fitting in keratoconus.

PubMed

Ortiz-Toquero, Sara; Rodriguez, Guadalupe; de Juan, Victoria; Martin, Raul

2017-06-01

To calculate and validate a new web-based algorithm for selecting the back optic zone radius (BOZR) of spherical gas permeable (GP) lens in keratoconus eyes. A retrospective calculation (n=35; multiple regression analysis) and a posterior prospective validation (new sample of 50 keratoconus eyes) of a new algorithm to select the BOZR of spherical KAKC design GP lenses (Conoptica) in keratoconus were conducted. BOZR calculated with the new algorithm, manufacturer guidelines and APEX software were compared with the BOZR that was finally prescribed. Number of diagnostic lenses, ordered lenses and visits to achieve optimal fitting were recorded and compared those obtained for a control group [50 healthy eyes fitted with spherical GP (BIAS design; Conoptica)]. The new algorithm highly correlated with the final BOZR fitted (r 2 =0.825, p<0.001). BOZR of the first diagnostic lens using the new algorithm demonstrated lower difference with the final BOZR prescribed (-0.01±0.12mm, p=0.65; 58% difference≤0.05mm) than with the manufacturer guidelines (+0.12±0.22mm, p<0.001; 26% difference≤0.05mm) and APEX software (-0.14±0.16mm, p=0.001; 34% difference≤0.05mm). Close numbers of diagnostic lens (1.6±0.8, 1.3±0.5; p=0.02), ordered lens (1.4±0.6, 1.1±0.3; P<0.001), and visits (3.4±0.7, 3.2±0.4; p=0.08) were required to fit keratoconus and healthy eyes, respectively. This new algorithm (free access at www.calculens.com) improves spherical KAKC GP fitting in keratoconus and can reduce the practitioner and patient chair time to achieve a final acceptable fit in keratoconus. This algorithm reduces differences between keratoconus GP fitting (KAKC design) and standard GP (BIAS design) lenses fitting in healthy eyes. Copyright © 2016 British Contact Lens Association. Published by Elsevier Ltd. All rights reserved.

A simple algorithm for beam profile diagnostics using a thermographic camera

DOE Office of Scientific and Technical Information (OSTI.GOV)

Katagiri, Ken; Hojo, Satoru; Honma, Toshihiro

2014-03-15

A new algorithm for digital image processing apparatuses is developed to evaluate profiles of high-intensity DC beams from temperature images of irradiated thin foils. Numerical analyses are performed to examine the reliability of the algorithm. To simulate the temperature images acquired by a thermographic camera, temperature distributions are numerically calculated for 20 MeV proton beams with different parameters. Noise in the temperature images which is added by the camera sensor is also simulated to account for its effect. Using the algorithm, beam profiles are evaluated from the simulated temperature images and compared with exact solutions. We find that niobium ismore » an appropriate material for the thin foil used in the diagnostic system. We also confirm that the algorithm is adaptable over a wide beam current range of 0.11–214 μA, even when employing a general-purpose thermographic camera with rather high noise (ΔT{sub NETD} ≃ 0.3 K; NETD: noise equivalent temperature difference)« less

Improving the Specificity of Plasmodium falciparum Malaria Diagnosis in High-Transmission Settings with a Two-Step Rapid Diagnostic Test and Microscopy Algorithm.

PubMed

Murungi, Moses; Fulton, Travis; Reyes, Raquel; Matte, Michael; Ntaro, Moses; Mulogo, Edgar; Nyehangane, Dan; Juliano, Jonathan J; Siedner, Mark J; Boum, Yap; Boyce, Ross M

2017-05-01

Poor specificity may negatively impact rapid diagnostic test (RDT)-based diagnostic strategies for malaria. We performed real-time PCR on a subset of subjects who had undergone diagnostic testing with a multiple-antigen (histidine-rich protein 2 and pan -lactate dehydrogenase pLDH [HRP2/pLDH]) RDT and microscopy. We determined the sensitivity and specificity of the RDT in comparison to results of PCR for the detection of Plasmodium falciparum malaria. We developed and evaluated a two-step algorithm utilizing the multiple-antigen RDT to screen patients, followed by confirmatory microscopy for those individuals with HRP2-positive (HRP2 + )/pLDH-negative (pLDH - ) results. In total, dried blood spots (DBS) were collected from 276 individuals. There were 124 (44.9%) individuals with an HRP2 + /pLDH + result, 94 (34.1%) with an HRP2 + /pLDH - result, and 58 (21%) with a negative RDT result. The sensitivity and specificity of the RDT compared to results with real-time PCR were 99.4% (95% confidence interval [CI], 95.9 to 100.0%) and 46.7% (95% CI, 37.7 to 55.9%), respectively. Of the 94 HRP2 + /pLDH - results, only 32 (34.0%) and 35 (37.2%) were positive by microscopy and PCR, respectively. The sensitivity and specificity of the two-step algorithm compared to results with real-time PCR were 95.5% (95% CI, 90.5 to 98.0%) and 91.0% (95% CI, 84.1 to 95.2), respectively. HRP2 antigen bands demonstrated poor specificity for the diagnosis of malaria compared to that of real-time PCR in a high-transmission setting. The most likely explanation for this finding is the persistence of HRP2 antigenemia following treatment of an acute infection. The two-step diagnostic algorithm utilizing microscopy as a confirmatory test for indeterminate HRP2 + /pLDH - results showed significantly improved specificity with little loss of sensitivity in a high-transmission setting. Copyright © 2017 American Society for Microbiology.

Sex-specific performance of pre-imaging diagnostic algorithms for pulmonary embolism.

PubMed

van Mens, T E; van der Pol, L M; van Es, N; Bistervels, I M; Mairuhu, A T A; van der Hulle, T; Klok, F A; Huisman, M V; Middeldorp, S

2018-05-01

Essentials Decision rules for pulmonary embolism are used indiscriminately despite possible sex-differences. Various pre-imaging diagnostic algorithms have been investigated in several prospective studies. When analysed at an individual patient data level the algorithms perform similarly in both sexes. Estrogen use and male sex were associated with a higher prevalence in suspected pulmonary embolism. Background In patients suspected of pulmonary embolism (PE), clinical decision rules are combined with D-dimer testing to rule out PE, avoiding the need for imaging in those at low risk. Despite sex differences in several aspects of the disease, including its diagnosis, these algorithms are used indiscriminately in women and men. Objectives To compare the performance, defined as efficiency and failure rate, of three pre-imaging diagnostic algorithms for PE between women and men: the Wells rule with fixed or with age-adjusted D-dimer cut-off, and a recently validated algorithm (YEARS). A secondary aim was to determine the sex-specific prevalence of PE. Methods Individual patient data were obtained from six studies using the Wells rule (fixed D-dimer, n = 5; age adjusted, n = 1) and from one study using the YEARS algorithm. All studies prospectively enrolled consecutive patients with suspected PE. Main outcomes were efficiency (proportion of patients in which the algorithm ruled out PE without imaging) and failure rate (proportion of patients with PE not detected by the algorithm). Outcomes were estimated using (multilevel) logistic regression models. Results The main outcomes showed no sex differences in any of the separate algorithms. With all three, the prevalence of PE was lower in women (OR, 0.66, 0.68 and 0.74). In women, estrogen use, adjusted for age, was associated with lower efficiency and higher prevalence and D-dimer levels. Conclusions The investigated pre-imaging diagnostic algorithms for patients suspected of PE show no sex differences in performance. Male sex and estrogen use are both associated with a higher probability of having the disease. © 2018 International Society on Thrombosis and Haemostasis.

Towards a Standard Psychometric Diagnostic Interview for Subjects at Ultra High Risk of Psychosis: CAARMS versus SIPS

PubMed Central

Fusar-Poli, P.; Cappucciati, M.; Rutigliano, G.; Lee, T. Y.; Beverly, Q.; Bonoldi, I.; Lelli, J.; Kaar, S. J.; Gago, E.; Rocchetti, M.; Patel, R.; Bhavsar, V.; Tognin, S.; Badger, S.; Calem, M.; Lim, K.; Kwon, J. S.; Perez, J.; McGuire, P.

2016-01-01

Background. Several psychometric instruments are available for the diagnostic interview of subjects at ultra high risk (UHR) of psychosis. Their diagnostic comparability is unknown. Methods. All referrals to the OASIS (London) or CAMEO (Cambridgeshire) UHR services from May 13 to Dec 14 were interviewed for a UHR state using both the CAARMS 12/2006 and the SIPS 5.0. Percent overall agreement, kappa, the McNemar-Bowker χ 2 test, equipercentile methods, and residual analyses were used to investigate diagnostic outcomes and symptoms severity or frequency. A conversion algorithm (CONVERT) was validated in an independent UHR sample from the Seoul Youth Clinic (Seoul). Results. There was overall substantial CAARMS-versus-SIPS agreement in the identification of UHR subjects (n = 212, percent overall agreement = 86%; kappa = 0.781, 95% CI from 0.684 to 0.878; McNemar-Bowker test = 0.069), with the exception of the brief limited intermittent psychotic symptoms (BLIPS) subgroup. Equipercentile-linking table linked symptoms severity and frequency across the CAARMS and SIPS. The conversion algorithm was validated in 93 UHR subjects, showing excellent diagnostic accuracy (CAARMS to SIPS: ROC area 0.929; SIPS to CAARMS: ROC area 0.903). Conclusions. This study provides initial comparability data between CAARMS and SIPS and will inform ongoing multicentre studies and clinical guidelines for the UHR psychometric diagnostic interview. PMID:27314005

Pathways to multidrug-resistant tuberculosis diagnosis and treatment initiation: a qualitative comparison of patients' experiences in the era of rapid molecular diagnostic tests.

PubMed

Naidoo, Pren; van Niekerk, Margaret; du Toit, Elizabeth; Beyers, Nulda; Leon, Natalie

2015-10-28

Although new molecular diagnostic tests such as GenoType MTBDRplus and Xpert® MTB/RIF have reduced multidrug-resistant tuberculosis (MDR-TB) treatment initiation times, patients' experiences of diagnosis and treatment initiation are not known. This study aimed to explore and compare MDR-TB patients' experiences of their diagnostic and treatment initiation pathway in GenoType MTBDRplus and Xpert® MTB/RIF-based diagnostic algorithms. The study was undertaken in Cape Town, South Africa where primary health-care services provided free TB diagnosis and treatment. A smear, culture and GenoType MTBDRplus diagnostic algorithm was used in 2010, with Xpert® MTB/RIF phased in from 2011-2013. Participants diagnosed in each algorithm at four facilities were purposively sampled, stratifying by age, gender and MDR-TB risk profiles. We conducted in-depth qualitative interviews using a semi-structured interview guide. Through constant comparative analysis we induced common and divergent themes related to symptom recognition, health-care access, testing for MDR-TB and treatment initiation within and between groups. Data were triangulated with clinical information and health visit data from a structured questionnaire. We identified both enablers and barriers to early MDR-TB diagnosis and treatment. Half the patients had previously been treated for TB; most recognised recurring symptoms and reported early health-seeking. Those who attributed symptoms to other causes delayed health-seeking. Perceptions of poor public sector services were prevalent and may have contributed both to deferred health-seeking and to patient's use of the private sector, contributing to delays. However, once on treatment, most patients expressed satisfaction with public sector care. Two patients in the Xpert® MTB/RIF-based algorithm exemplified its potential to reduce delays, commencing MDR-TB treatment within a week of their first health contact. However, most patients in both algorithms experienced substantial delays. Avoidable health system delays resulted from providers not testing for TB at initial health contact, non-adherence to testing algorithms, results not being available and failure to promptly recall patients with positive results. Whilst the introduction of rapid tests such as Xpert® MTB/RIF can expedite MDR-TB diagnosis and treatment initiation, the full benefits are unlikely to be realised without reducing delays in health-seeking and addressing the structural barriers present in the health-care system.

A whole-heart motion-correction algorithm: Effects on CT image quality and diagnostic accuracy of mechanical valve prosthesis abnormalities.

PubMed

Suh, Young Joo; Kim, Young Jin; Kim, Jin Young; Chang, Suyon; Im, Dong Jin; Hong, Yoo Jin; Choi, Byoung Wook

2017-11-01

We aimed to determine the effect of a whole-heart motion-correction algorithm (new-generation snapshot freeze, NG SSF) on the image quality of cardiac computed tomography (CT) images in patients with mechanical valve prostheses compared to standard images without motion correction and to compare the diagnostic accuracy of NG SSF and standard CT image sets for the detection of prosthetic valve abnormalities. A total of 20 patients with 32 mechanical valves who underwent wide-coverage detector cardiac CT with single-heartbeat acquisition were included. The CT image quality for subvalvular (below the prosthesis) and valvular regions (valve leaflets) of mechanical valves was assessed by two observers on a four-point scale (1 = poor, 2 = fair, 3 = good, and 4 = excellent). Paired t-tests or Wilcoxon signed rank tests were used to compare image quality scores and the number of diagnostic phases (image quality score≥3) between the standard image sets and NG SSF image sets. Diagnostic performance for detection of prosthetic valve abnormalities was compared between two image sets with the final diagnosis set by re-operation or clinical findings as the standard reference. NG SSF image sets had better image quality scores than standard image sets for both valvular and subvalvular regions (P < 0.05 for both). The number of phases that were of diagnostic image quality per patient was significantly greater in the NG SSF image set than standard image set for both valvular and subvalvular regions (P < 0.0001). Diagnostic performance of NG SSF image sets for the detection of prosthetic abnormalities (20 pannus and two paravalvular leaks) was greater than that of standard image sets (P < 0.05). Application of NG SSF can improve CT image quality and diagnostic accuracy in patients with mechanical valves compared to standard images. Copyright © 2017 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy.

PubMed

Hesar, Hamed Danandeh; Mohebbi, Maryam

2017-05-01

In this paper, a model-based Bayesian filtering framework called the "marginalized particle-extended Kalman filter (MP-EKF) algorithm" is proposed for electrocardiogram (ECG) denoising. This algorithm does not have the extended Kalman filter (EKF) shortcoming in handling non-Gaussian nonstationary situations because of its nonlinear framework. In addition, it has less computational complexity compared with particle filter. This filter improves ECG denoising performance by implementing marginalized particle filter framework while reducing its computational complexity using EKF framework. An automatic particle weighting strategy is also proposed here that controls the reliance of our framework to the acquired measurements. We evaluated the proposed filter on several normal ECGs selected from MIT-BIH normal sinus rhythm database. To do so, artificial white Gaussian and colored noises as well as nonstationary real muscle artifact (MA) noise over a range of low SNRs from 10 to -5 dB were added to these normal ECG segments. The benchmark methods were the EKF and extended Kalman smoother (EKS) algorithms which are the first model-based Bayesian algorithms introduced in the field of ECG denoising. From SNR viewpoint, the experiments showed that in the presence of Gaussian white noise, the proposed framework outperforms the EKF and EKS algorithms in lower input SNRs where the measurements and state model are not reliable. Owing to its nonlinear framework and particle weighting strategy, the proposed algorithm attained better results at all input SNRs in non-Gaussian nonstationary situations (such as presence of pink noise, brown noise, and real MA). In addition, the impact of the proposed filtering method on the distortion of diagnostic features of the ECG was investigated and compared with EKF/EKS methods using an ECG diagnostic distortion measure called the "Multi-Scale Entropy Based Weighted Distortion Measure" or MSEWPRD. The results revealed that our proposed algorithm had the lowest MSEPWRD for all noise types at low input SNRs. Therefore, the morphology and diagnostic information of ECG signals were much better conserved compared with EKF/EKS frameworks, especially in non-Gaussian nonstationary situations.

Integration of On-Line and Off-Line Diagnostic Algorithms for Aircraft Engine Health Management

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2007-01-01

This paper investigates the integration of on-line and off-line diagnostic algorithms for aircraft gas turbine engines. The on-line diagnostic algorithm is designed for in-flight fault detection. It continuously monitors engine outputs for anomalous signatures induced by faults. The off-line diagnostic algorithm is designed to track engine health degradation over the lifetime of an engine. It estimates engine health degradation periodically over the course of the engine s life. The estimate generated by the off-line algorithm is used to update the on-line algorithm. Through this integration, the on-line algorithm becomes aware of engine health degradation, and its effectiveness to detect faults can be maintained while the engine continues to degrade. The benefit of this integration is investigated in a simulation environment using a nonlinear engine model.

Is introducing rapid culture into the diagnostic algorithm of smear-negative tuberculosis cost-effective?

PubMed

Yakhelef, N; Audibert, M; Varaine, F; Chakaya, J; Sitienei, J; Huerga, H; Bonnet, M

2014-05-01

In 2007, the World Health Organization recommended introducing rapid Mycobacterium tuberculosis culture into the diagnostic algorithm of smear-negative pulmonary tuberculosis (TB). To assess the cost-effectiveness of introducing a rapid non-commercial culture method (thin-layer agar), together with Löwenstein-Jensen culture to diagnose smear-negative TB at a district hospital in Kenya. Outcomes (number of true TB cases treated) were obtained from a prospective study evaluating the effectiveness of a clinical and radiological algorithm (conventional) against the alternative algorithm (conventional plus M. tuberculosis culture) in 380 smear-negative TB suspects. The costs of implementing each algorithm were calculated using a 'micro-costing' or 'ingredient-based' method. We then compared the cost and effectiveness of conventional vs. culture-based algorithms and estimated the incremental cost-effectiveness ratio. The costs of conventional and culture-based algorithms per smear-negative TB suspect were respectively €39.5 and €144. The costs per confirmed and treated TB case were respectively €452 and €913. The culture-based algorithm led to diagnosis and treatment of 27 more cases for an additional cost of €1477 per case. Despite the increase in patients started on treatment thanks to culture, the relatively high cost of a culture-based algorithm will make it difficult for resource-limited countries to afford.

Diagnostic Algorithm Benchmarking

NASA Technical Reports Server (NTRS)

Poll, Scott

2011-01-01

A poster for the NASA Aviation Safety Program Annual Technical Meeting. It describes empirical benchmarking on diagnostic algorithms using data from the ADAPT Electrical Power System testbed and a diagnostic software framework.

The Autism Diagnostic Observation Schedule, Module 4: Revised Algorithm and Standardized Severity Scores

ERIC Educational Resources Information Center

Hus, Vanessa; Lord, Catherine

2014-01-01

The recently published Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2) includes revised diagnostic algorithms and standardized severity scores for modules used to assess younger children. A revised algorithm and severity scores are not yet available for Module 4, used with verbally fluent adults. The current study revises the Module 4…

Improved Diagnostic Validity of the ADOS Revised Algorithms: A Replication Study in an Independent Sample

ERIC Educational Resources Information Center

Oosterling, Iris; Roos, Sascha; de Bildt, Annelies; Rommelse, Nanda; de Jonge, Maretha; Visser, Janne; Lappenschaar, Martijn; Swinkels, Sophie; van der Gaag, Rutger Jan; Buitelaar, Jan

2010-01-01

Recently, Gotham et al. ("2007") proposed revised algorithms for the Autism Diagnostic Observation Schedule (ADOS) with improved diagnostic validity. The aim of the current study was to replicate predictive validity, factor structure, and correlations with age and verbal and nonverbal IQ of the ADOS revised algorithms for Modules 1 and 2…

Systematic Benchmarking of Diagnostic Technologies for an Electrical Power System

NASA Technical Reports Server (NTRS)

Kurtoglu, Tolga; Jensen, David; Poll, Scott

2009-01-01

Automated health management is a critical functionality for complex aerospace systems. A wide variety of diagnostic algorithms have been developed to address this technical challenge. Unfortunately, the lack of support to perform large-scale V&V (verification and validation) of diagnostic technologies continues to create barriers to effective development and deployment of such algorithms for aerospace vehicles. In this paper, we describe a formal framework developed for benchmarking of diagnostic technologies. The diagnosed system is the Advanced Diagnostics and Prognostics Testbed (ADAPT), a real-world electrical power system (EPS), developed and maintained at the NASA Ames Research Center. The benchmarking approach provides a systematic, empirical basis to the testing of diagnostic software and is used to provide performance assessment for different diagnostic algorithms.

Non-invasive optical detection of esophagus cancer based on urine surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Shaohua; Wang, Lan; Chen, Weiwei; Lin, Duo; Huang, Lingling; Wu, Shanshan; Feng, Shangyuan; Chen, Rong

2014-09-01

A surface-enhanced Raman spectroscopy (SERS) approach was utilized for urine biochemical analysis with the aim to develop a label-free and non-invasive optical diagnostic method for esophagus cancer detection. SERS spectrums were acquired from 31 normal urine samples and 47 malignant esophagus cancer (EC) urine samples. Tentative assignments of urine SERS bands demonstrated esophagus cancer specific changes, including an increase in the relative amounts of urea and a decrease in the percentage of uric acid in the urine of normal compared with EC. The empirical algorithm integrated with linear discriminant analysis (LDA) were employed to identify some important urine SERS bands for differentiation between healthy subjects and EC urine. The empirical diagnostic approach based on the ratio of the SERS peak intensity at 527 to 1002 cm-1 and 725 to 1002 cm-1 coupled with LDA yielded a diagnostic sensitivity of 72.3% and specificity of 96.8%, respectively. The area under the receive operating characteristic (ROC) curve was 0.954, which further evaluate the performance of the diagnostic algorithm based on the ratio of the SERS peak intensity combined with LDA analysis. This work demonstrated that the urine SERS spectra associated with empirical algorithm has potential for noninvasive diagnosis of esophagus cancer.

Autism in the Faroe Islands: Diagnostic Stability from Childhood to Early Adult Life

PubMed Central

Kočovská, Eva; Billstedt, Eva; Ellefsen, Asa; Kampmann, Hanna; Gillberg, I. Carina; Biskupstø, Rannvá; Andorsdóttir, Guðrið; Stóra, Tormóður; Minnis, Helen; Gillberg, Christopher

2013-01-01

Childhood autism or autism spectrum disorder (ASD) has been regarded as one of the most stable diagnostic categories applied to young children with psychiatric/developmental disorders. The stability over time of a diagnosis of ASD is theoretically interesting and important for various diagnostic and clinical reasons. We studied the diagnostic stability of ASD from childhood to early adulthood in the Faroe Islands: a total school age population sample (8–17-year-olds) was screened and diagnostically assessed for AD in 2002 and 2009. This paper compares both independent clinical diagnosis and Diagnostic Interview for Social and Communication Disorders (DISCO) algorithm diagnosis at two time points, separated by seven years. The stability of clinical ASD diagnosis was perfect for AD, good for “atypical autism”/PDD-NOS, and less than perfect for Asperger syndrome (AS). Stability of the DISCO algorithm subcategory diagnoses was more variable but still good for AD. Both systems showed excellent stability over the seven-year period for “any ASD” diagnosis, although a number of clear cases had been missed at the original screening in 2002. The findings support the notion that subcategories of ASD should be collapsed into one overarching diagnostic entity with subgrouping achieved on other “non-autism” variables, such as IQ and language levels and overall adaptive functioning. PMID:23476144

Accounting for False Positive HIV Tests: Is Visceral Leishmaniasis Responsible?

PubMed Central

Shanks, Leslie; Ritmeijer, Koert; Piriou, Erwan; Siddiqui, M. Ruby; Kliescikova, Jarmila; Pearce, Neil; Ariti, Cono; Muluneh, Libsework; Masiga, Johnson; Abebe, Almaz

2015-01-01

Background Co-infection with HIV and visceral leishmaniasis is an important consideration in treatment of either disease in endemic areas. Diagnosis of HIV in resource-limited settings relies on rapid diagnostic tests used together in an algorithm. A limitation of the HIV diagnostic algorithm is that it is vulnerable to falsely positive reactions due to cross reactivity. It has been postulated that visceral leishmaniasis (VL) infection can increase this risk of false positive HIV results. This cross sectional study compared the risk of false positive HIV results in VL patients with non-VL individuals. Methodology/Principal Findings Participants were recruited from 2 sites in Ethiopia. The Ethiopian algorithm of a tiebreaker using 3 rapid diagnostic tests (RDTs) was used to test for HIV. The gold standard test was the Western Blot, with indeterminate results resolved by PCR testing. Every RDT screen positive individual was included for testing with the gold standard along with 10% of all negatives. The final analysis included 89 VL and 405 non-VL patients. HIV prevalence was found to be 12.8% (47/ 367) in the VL group compared to 7.9% (200/2526) in the non-VL group. The RDT algorithm in the VL group yielded 47 positives, 4 false positives, and 38 negatives. The same algorithm for those without VL had 200 positives, 14 false positives, and 191 negatives. Specificity and positive predictive value for the group with VL was less than the non-VL group; however, the difference was not found to be significant (p = 0.52 and p = 0.76, respectively). Conclusion The test algorithm yielded a high number of HIV false positive results. However, we were unable to demonstrate a significant difference between groups with and without VL disease. This suggests that the presence of endemic visceral leishmaniasis alone cannot account for the high number of false positive HIV results in our study. PMID:26161864

Accounting for False Positive HIV Tests: Is Visceral Leishmaniasis Responsible?

PubMed

Shanks, Leslie; Ritmeijer, Koert; Piriou, Erwan; Siddiqui, M Ruby; Kliescikova, Jarmila; Pearce, Neil; Ariti, Cono; Muluneh, Libsework; Masiga, Johnson; Abebe, Almaz

2015-01-01

Co-infection with HIV and visceral leishmaniasis is an important consideration in treatment of either disease in endemic areas. Diagnosis of HIV in resource-limited settings relies on rapid diagnostic tests used together in an algorithm. A limitation of the HIV diagnostic algorithm is that it is vulnerable to falsely positive reactions due to cross reactivity. It has been postulated that visceral leishmaniasis (VL) infection can increase this risk of false positive HIV results. This cross sectional study compared the risk of false positive HIV results in VL patients with non-VL individuals. Participants were recruited from 2 sites in Ethiopia. The Ethiopian algorithm of a tiebreaker using 3 rapid diagnostic tests (RDTs) was used to test for HIV. The gold standard test was the Western Blot, with indeterminate results resolved by PCR testing. Every RDT screen positive individual was included for testing with the gold standard along with 10% of all negatives. The final analysis included 89 VL and 405 non-VL patients. HIV prevalence was found to be 12.8% (47/ 367) in the VL group compared to 7.9% (200/2526) in the non-VL group. The RDT algorithm in the VL group yielded 47 positives, 4 false positives, and 38 negatives. The same algorithm for those without VL had 200 positives, 14 false positives, and 191 negatives. Specificity and positive predictive value for the group with VL was less than the non-VL group; however, the difference was not found to be significant (p = 0.52 and p = 0.76, respectively). The test algorithm yielded a high number of HIV false positive results. However, we were unable to demonstrate a significant difference between groups with and without VL disease. This suggests that the presence of endemic visceral leishmaniasis alone cannot account for the high number of false positive HIV results in our study.

Measurement of fecal elastase improves performance of newborn screening for cystic fibrosis.

PubMed

Barben, Juerg; Rueegg, Corina S; Jurca, Maja; Spalinger, Johannes; Kuehni, Claudia E

2016-05-01

The aim of newborn screening (NBS) for CF is to detect children with 'classic' CF where early treatment is possible and improves prognosis. Children with inconclusive CF diagnosis (CFSPID) should not be detected, as there is no evidence for improvement through early treatment. No algorithm in current NBS guidelines explains what to do when sweat test (ST) fails. This study compares the performance of three different algorithms for further diagnostic evaluations when first ST is unsuccessful, regarding the numbers of children detected with CF and CFSPID, and the time until a definite diagnosis. In Switzerland, CF-NBS was introduced in January 2011 using an IRT-DNA-IRT algorithm followed by a ST. In children, in whom ST was not possible (no or insufficient sweat), 3 different protocols were applied between 2011 and 2014: in 2011, ST was repeated until it was successful (protocol A), in 2012 we proceeded directly to diagnostic DNA testing (protocol B), and 2013-2014, fecal elastase (FE) was measured in the stool, in order to determine a pancreas insufficiency needing immediate treatment (protocol C). The ratio CF:CFSPID was 7:1 (27/4) with protocol A, 2:1 (22/10) with protocol B, and 14:1 (54/4) with protocol C. The mean time to definite diagnosis was significantly shorter with protocol C (33days) compared to protocol A or B (42 and 40days; p=0.014 compared to A, and p=0.036 compared to B). The algorithm for the diagnostic part of the newborn screening used in the CF centers is important and affects the performance of a CF-NBS program with regard to the ratio CF:CFSPID and the time until definite diagnosis. Our results suggest to include FE after initial sweat test failure in the CF-NBS guidelines to keep the proportion of CFSPID low and the time until definite diagnosis short. Copyright © 2016 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

The PHQ-PD as a Screening Tool for Panic Disorder in the Primary Care Setting in Spain

PubMed Central

Wood, Cristina Mae; Ruíz-Rodríguez, Paloma; Tomás-Tomás, Patricia; Gracia-Gracia, Irene; Dongil-Collado, Esperanza; Iruarrizaga, M. Iciar

2016-01-01

Introduction Panic disorder is a common anxiety disorder and is highly prevalent in Spanish primary care centres. The use of validated tools can improve the detection of panic disorder in primary care populations, thus enabling referral for specialized treatment. The aim of this study is to determine the accuracy of the Patient Health Questionnaire-Panic Disorder (PHQ-PD) as a screening and diagnostic tool for panic disorder in Spanish primary care centres. Method We compared the psychometric properties of the PHQ-PD to the reference standard, the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) interview. General practitioners referred 178 patients who completed the entire PHQ test, including the PHQ-PD, to undergo the SCID-I. The sensitivity, specificity, positive and negative predictive values and positive and negative likelihood ratios of the PHQ-PD were assessed. Results The operating characteristics of the PHQ-PD are moderate. The best cut-off score was 5 (sensitivity .77, specificity .72). Modifications to the questionnaire's algorithms improved test characteristics (sensitivity .77, specificity .72) compared to the original algorithm. The screening question alone yielded the highest sensitivity score (.83). Conclusion Although the modified algorithm of the PHQ-PD only yielded moderate results as a diagnostic test for panic disorder, it was better than the original. Using only the first question of the PHQ-PD showed the best psychometric properties (sensitivity). Based on these findings, we suggest the use of the screening questions for screening purposes and the modified algorithm for diagnostic purposes. PMID:27525977

Efficacy and Clinical Utility of a High-Attenuation Object Artifact Reduction Algorithm in Flat-Detector Image Reconstruction Compared With Standard Image Reconstruction.

PubMed

Naehle, Claas P; Hechelhammer, Lukas; Richter, Heiko; Ryffel, Fabian; Wildermuth, Simon; Weber, Johannes

To evaluate the effectiveness and clinical utility of a metal artifact reduction (MAR) image reconstruction algorithm for the reduction of high-attenuation object (HAO)-related image artifacts. Images were quantitatively evaluated for image noise (noiseSD and noiserange) and qualitatively for artifact severity, gray-white-matter delineation, and diagnostic confidence with conventional reconstruction and after applying a MAR algorithm. Metal artifact reduction reduces noiseSD and noiserange (median [interquartile range]) at the level of HAO in 1-cm distance compared with conventional reconstruction (noiseSD: 60.0 [71.4] vs 12.8 [16.1] and noiserange: 262.0 [236.8] vs 72.0 [28.3]; P < 0.0001). Artifact severity (reader 1 [mean ± SD]: 1.1 ± 0.6 vs 2.4 ± 0.5, reader 2: 0.8 ± 0.6 vs 2.0 ± 0.4) at level of HAO and diagnostic confidence (reader 1: 1.6 ± 0.7 vs 2.6 ± 0.5, reader 2: 1.0 ± 0.6 vs 2.3 ± 0.7) significantly improved with MAR (P < 0.0001). Metal artifact reduction did not affect gray-white-matter delineation. Metal artifact reduction effectively reduces image artifacts caused by HAO and significantly improves diagnostic confidence without worsening gray-white-matter delineation.

The Added Value of the Combined Use of the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule: Diagnostic Validity in a Clinical Swedish Sample of Toddlers and Young Preschoolers

ERIC Educational Resources Information Center

Zander, Eric; Sturm, Harald; Bölte, Sven

2015-01-01

The diagnostic validity of the new research algorithms of the Autism Diagnostic Interview-Revised and the revised algorithms of the Autism Diagnostic Observation Schedule was examined in a clinical sample of children aged 18-47 months. Validity was determined for each instrument separately and their combination against a clinical consensus…

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of an Enhanced System

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2008-01-01

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system.

Impact of iterative metal artifact reduction on diagnostic image quality in patients with dental hardware.

PubMed

Weiß, Jakob; Schabel, Christoph; Bongers, Malte; Raupach, Rainer; Clasen, Stephan; Notohamiprodjo, Mike; Nikolaou, Konstantin; Bamberg, Fabian

2017-03-01

Background Metal artifacts often impair diagnostic accuracy in computed tomography (CT) imaging. Therefore, effective and workflow implemented metal artifact reduction algorithms are crucial to gain higher diagnostic image quality in patients with metallic hardware. Purpose To assess the clinical performance of a novel iterative metal artifact reduction (iMAR) algorithm for CT in patients with dental fillings. Material and Methods Thirty consecutive patients scheduled for CT imaging and dental fillings were included in the analysis. All patients underwent CT imaging using a second generation dual-source CT scanner (120 kV single-energy; 100/Sn140 kV in dual-energy, 219 mAs, gantry rotation time 0.28-1/s, collimation 0.6 mm) as part of their clinical work-up. Post-processing included standard kernel (B49) and an iterative MAR algorithm. Image quality and diagnostic value were assessed qualitatively (Likert scale) and quantitatively (HU ± SD) by two reviewers independently. Results All 30 patients were included in the analysis, with equal reconstruction times for iMAR and standard reconstruction (17 s ± 0.5 vs. 19 s ± 0.5; P > 0.05). Visual image quality was significantly higher for iMAR as compared with standard reconstruction (3.8 ± 0.5 vs. 2.6 ± 0.5; P < 0.0001, respectively) and showed improved evaluation of adjacent anatomical structures. Similarly, HU-based measurements of degree of artifacts were significantly lower in the iMAR reconstructions as compared with the standard reconstruction (0.9 ± 1.6 vs. -20 ± 47; P < 0.05, respectively). Conclusion The tested iterative, raw-data based reconstruction MAR algorithm allows for a significant reduction of metal artifacts and improved evaluation of adjacent anatomical structures in the head and neck area in patients with dental hardware.

New Autism Diagnostic Interview-Revised Algorithms for Toddlers and Young Preschoolers from 12 to 47 Months of Age

ERIC Educational Resources Information Center

Kim, So Hyun; Lord, Catherine

2012-01-01

Autism Diagnostic Interview-Revised (Rutter et al. in "Autism diagnostic interview-revised." Western Psychological Services, Los Angeles, 2003) diagnostic algorithms specific to toddlers and young preschoolers were created using 829 assessments of children aged from 12 to 47 months with ASD, nonspectrum disorders, and typical development. The…

Usefulness of magnifying endoscopy with narrow-band imaging for diagnosis of depressed gastric lesions

PubMed Central

SUMIE, HIROAKI; SUMIE, SHUJI; NAKAHARA, KEITA; WATANABE, YASUTOMO; MATSUO, KEN; MUKASA, MICHITA; SAKAI, TAKESHI; YOSHIDA, HIKARU; TSURUTA, OSAMU; SATA, MICHIO

2014-01-01

The usefulness of magnifying endoscopy with narrow-band imaging (ME-NBI) for the diagnosis of early gastric cancer is well known, however, there are no evaluation criteria. The aim of this study was to devise and evaluate a novel diagnostic algorithm for ME-NBI in depressed early gastric cancer. Between August, 2007 and May, 2011, 90 patients with a total of 110 depressed gastric lesions were enrolled in the study. A diagnostic algorithm was devised based on ME-NBI microvascular findings: microvascular irregularity and abnormal microvascular patterns (fine network, corkscrew and unclassified patterns). The diagnostic efficiency of the algorithm for gastric cancer and histological grade was assessed by measuring its mean sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Furthermore, inter- and intra-observer variation were measured. In the differential diagnosis of gastric cancer from non-cancerous lesions, the mean sensitivity, specificity, PPV, NPV, and accuracy of the diagnostic algorithm were 86.7, 48.0, 94.4, 26.7, and 83.2%, respectively. Furthermore, in the differential diagnosis of undifferentiated adenocarcinoma from differentiated adenocarcinoma, the mean sensitivity, specificity, PPV, NPV, and accuracy of the diagnostic algorithm were 61.6, 86.3, 69.0, 84.8, and 79.1%, respectively. For the ME-NBI final diagnosis using this algorithm, the mean κ values for inter- and intra-observer agreement were 0.50 and 0.77, respectively. In conclusion, the diagnostic algorithm based on ME-NBI microvascular findings was convenient and had high diagnostic accuracy, reliability and reproducibility in the differential diagnosis of depressed gastric lesions. PMID:24649321

Ad hoc cost analysis of the new gastrointestinal bleeding algorithm in patients with ventricular assist device.

PubMed

Hirose, Hitoshi; Sarosiek, Konrad; Cavarocchi, Nicholas C

2014-01-01

Gastrointestinal bleed (GIB) is a known complication in patients receiving nonpulsatile ventricular assist devices (VAD). Previously, we reported a new algorithm for the workup of GIB in VAD patients using deep bowel enteroscopy. In this new algorithm, patients underwent fewer procedures, received less transfusions, and took less time to make the diagnosis than the traditional GIB algorithm group. Concurrently, we reviewed the cost-effectiveness of this new algorithm compared with the traditional workup. The procedure charges for the diagnosis and treatment of each episode of GIB was ~ $2,902 in the new algorithm group versus ~ $9,013 in the traditional algorithm group (p < 0.0001). Following the new algorithm in VAD patients with GIB resulted in fewer transfusions and diagnostic tests while attaining a substantial cost savings per episode of bleeding.

Clinical effectiveness of a Bayesian algorithm for the diagnosis and management of heparin-induced thrombocytopenia.

PubMed

Raschke, R A; Gallo, T; Curry, S C; Whiting, T; Padilla-Jones, A; Warkentin, T E; Puri, A

2017-08-01

Essentials We previously published a diagnostic algorithm for heparin-induced thrombocytopenia (HIT). In this study, we validated the algorithm in an independent large healthcare system. The accuracy was 98%, sensitivity 82% and specificity 99%. The algorithm has potential to improve accuracy and efficiency in the diagnosis of HIT. Background Heparin-induced thrombocytopenia (HIT) is a life-threatening drug reaction caused by antiplatelet factor 4/heparin (anti-PF4/H) antibodies. Commercial tests to detect these antibodies have suboptimal operating characteristics. We previously developed a diagnostic algorithm for HIT that incorporated 'four Ts' (4Ts) scoring and a stratified interpretation of an anti-PF4/H enzyme-linked immunosorbent assay (ELISA) and yielded a discriminant accuracy of 0.97 (95% confidence interval [CI], 0.93-1.00). Objectives The purpose of this study was to validate the algorithm in an independent patient population and quantitate effects that algorithm adherence could have on clinical care. Methods A retrospective cohort comprised patients who had undergone anti-PF4/H ELISA and serotonin release assay (SRA) testing in our healthcare system from 2010 to 2014. We determined the algorithm recommendation for each patient, compared recommendations with the clinical care received, and enumerated consequences of discrepancies. Operating characteristics were calculated for algorithm recommendations using SRA as the reference standard. Results Analysis was performed on 181 patients, 10 of whom were ruled in for HIT. The algorithm accurately stratified 98% of patients (95% CI, 95-99%), ruling out HIT in 158, ruling in HIT in 10 and recommending an SRA in 13 patients. Algorithm adherence would have obviated 165 SRAs and prevented 30 courses of unnecessary antithrombotic therapy for HIT. Diagnostic sensitivity was 0.82 (95% CI, 0.48-0.98), specificity 0.99 (95% CI, 0.97-1.00), PPV 0.90 (95% CI, 0.56-0.99) and NPV 0.99 (95% CI, 0.96-1.00). Conclusions An algorithm incorporating 4Ts scoring and a stratified interpretation of the anti-PF4/H ELISA has good operating characteristics and the potential to improve management of suspected HIT patients. © 2017 International Society on Thrombosis and Haemostasis.

Spatial enhancement of ECG using diagnostic similarity score based lead selective multi-scale linear model.

PubMed

Nallikuzhy, Jiss J; Dandapat, S

2017-06-01

In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advantage of the improved inter-lead correlation in wavelet domain. Since the model is patient-specific, it also selects the optimal predictor leads for a given patient using a lead selection algorithm. The lead selection algorithm is based on a new diagnostic similarity score which computes the diagnostic closeness between the original and the spatially enhanced leads. Standard closeness measures are used to assess the performance of the model. The similarity in diagnostic information between the original and the spatially enhanced leads are evaluated using various diagnostic measures. Repeatability and diagnosability are performed to quantify the applicability of the model. A comparison of the proposed model is performed with existing models that transform a subset of standard twelve-lead ECG into the standard twelve-lead ECG. From the analysis of the results, it is evident that the proposed model preserves diagnostic information better compared to other models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mental Health Risk Adjustment with Clinical Categories and Machine Learning.

PubMed

Shrestha, Akritee; Bergquist, Savannah; Montz, Ellen; Rose, Sherri

2017-12-15

To propose nonparametric ensemble machine learning for mental health and substance use disorders (MHSUD) spending risk adjustment formulas, including considering Clinical Classification Software (CCS) categories as diagnostic covariates over the commonly used Hierarchical Condition Category (HCC) system. 2012-2013 Truven MarketScan database. We implement 21 algorithms to predict MHSUD spending, as well as a weighted combination of these algorithms called super learning. The algorithm collection included seven unique algorithms that were supplied with three differing sets of MHSUD-related predictors alongside demographic covariates: HCC, CCS, and HCC + CCS diagnostic variables. Performance was evaluated based on cross-validated R 2 and predictive ratios. Results show that super learning had the best performance based on both metrics. The top single algorithm was random forests, which improved on ordinary least squares regression by 10 percent with respect to relative efficiency. CCS categories-based formulas were generally more predictive of MHSUD spending compared to HCC-based formulas. Literature supports the potential benefit of implementing a separate MHSUD spending risk adjustment formula. Our results suggest there is an incentive to explore machine learning for MHSUD-specific risk adjustment, as well as considering CCS categories over HCCs. © Health Research and Educational Trust.

Thoracoabdominal Computed Tomography in Trauma Patients: A Cost-Consequences Analysis

PubMed Central

van Vugt, Raoul; Kool, Digna R.; Brink, Monique; Dekker, Helena M.; Deunk, Jaap; Edwards, Michael J.

2014-01-01

Background: CT is increasingly used during the initial evaluation of blunt trauma patients. In this era of increasing cost-awareness, the pros and cons of CT have to be assessed. Objectives: This study was performed to evaluate cost-consequences of different diagnostic algorithms that use thoracoabdominal CT in primary evaluation of adult patients with high-energy blunt trauma. Materials and Methods: We compared three different algorithms in which CT was applied as an immediate diagnostic tool (rush CT), a diagnostic tool after limited conventional work-up (routine CT), and a selective tool (selective CT). Probabilities of detecting and missing clinically relevant injuries were retrospectively derived. We collected data on radiation exposure and performed a micro-cost analysis on a reference case-based approach. Results: Both rush and routine CT detected all thoracoabdominal injuries in 99.1% of the patients during primary evaluation (n = 1040). Selective CT missed one or more diagnoses in 11% of the patients in which a change of treatment was necessary in 4.8%. Rush CT algorithm costed € 2676 (US$ 3660) per patient with a mean radiation dose of 26.40 mSv per patient. Routine CT costed € 2815 (US$ 3850) and resulted in the same radiation exposure. Selective CT resulted in less radiation dose (23.23 mSv) and costed € 2771 (US$ 3790). Conclusions: Rush CT seems to result in the least costs and is comparable in terms of radiation dose exposure and diagnostic certainty with routine CT after a limited conventional work-up. However, selective CT results in less radiation dose exposure but a slightly higher cost and less certainty. PMID:25337521

The 10/66 Dementia Research Group's fully operationalised DSM-IV dementia computerized diagnostic algorithm, compared with the 10/66 dementia algorithm and a clinician diagnosis: a population validation study

PubMed Central

Prince, Martin J; de Rodriguez, Juan Llibre; Noriega, L; Lopez, A; Acosta, Daisy; Albanese, Emiliano; Arizaga, Raul; Copeland, John RM; Dewey, Michael; Ferri, Cleusa P; Guerra, Mariella; Huang, Yueqin; Jacob, KS; Krishnamoorthy, ES; McKeigue, Paul; Sousa, Renata; Stewart, Robert J; Salas, Aquiles; Sosa, Ana Luisa; Uwakwa, Richard

2008-01-01

Background The criterion for dementia implicit in DSM-IV is widely used in research but not fully operationalised. The 10/66 Dementia Research Group sought to do this using assessments from their one phase dementia diagnostic research interview, and to validate the resulting algorithm in a population-based study in Cuba. Methods The criterion was operationalised as a computerised algorithm, applying clinical principles, based upon the 10/66 cognitive tests, clinical interview and informant reports; the Community Screening Instrument for Dementia, the CERAD 10 word list learning and animal naming tests, the Geriatric Mental State, and the History and Aetiology Schedule – Dementia Diagnosis and Subtype. This was validated in Cuba against a local clinician DSM-IV diagnosis and the 10/66 dementia diagnosis (originally calibrated probabilistically against clinician DSM-IV diagnoses in the 10/66 pilot study). Results The DSM-IV sub-criteria were plausibly distributed among clinically diagnosed dementia cases and controls. The clinician diagnoses agreed better with 10/66 dementia diagnosis than with the more conservative computerized DSM-IV algorithm. The DSM-IV algorithm was particularly likely to miss less severe dementia cases. Those with a 10/66 dementia diagnosis who did not meet the DSM-IV criterion were less cognitively and functionally impaired compared with the DSMIV confirmed cases, but still grossly impaired compared with those free of dementia. Conclusion The DSM-IV criterion, strictly applied, defines a narrow category of unambiguous dementia characterized by marked impairment. It may be specific but incompletely sensitive to clinically relevant cases. The 10/66 dementia diagnosis defines a broader category that may be more sensitive, identifying genuine cases beyond those defined by our DSM-IV algorithm, with relevance to the estimation of the population burden of this disorder. PMID:18577205

Development of a novel diagnostic algorithm to predict NASH in HCV-positive patients.

PubMed

Gallotta, Andrea; Paneghetti, Laura; Mrázová, Viera; Bednárová, Adriana; Kružlicová, Dáša; Frecer, Vladimir; Miertus, Stanislav; Biasiolo, Alessandra; Martini, Andrea; Pontisso, Patrizia; Fassina, Giorgio

2018-05-01

Non-alcoholic steato-hepatitis (NASH) is a severe disease characterised by liver inflammation and progressive hepatic fibrosis, which may progress to cirrhosis and hepatocellular carcinoma. Clinical evidence suggests that in hepatitis C virus patients steatosis and NASH are associated with faster fibrosis progression and hepatocellular carcinoma. A safe and reliable non-invasive diagnostic method to detect NASH at its early stages is still needed to prevent progression of the disease. We prospectively enrolled 91 hepatitis C virus-positive patients with histologically proven chronic liver disease: 77 patients were included in our study; of these, 10 had NASH. For each patient, various clinical and serological variables were collected. Different algorithms combining squamous cell carcinoma antigen-immunoglobulin-M (SCCA-IgM) levels with other common clinical data were created to provide the probability of having NASH. Our analysis revealed a statistically significant correlation between the histological presence of NASH and SCCA-IgM, insulin, homeostasis model assessment, haemoglobin, high-density lipoprotein and ferritin levels, and smoke. Compared to the use of a single marker, algorithms that combined four, six or seven variables identified NASH with higher accuracy. The best diagnostic performance was obtained with the logistic regression combination, which included all seven variables correlated with NASH. The combination of SCCA-IgM with common clinical data shows promising diagnostic performance for the detection of NASH in hepatitis C virus patients.

The tradition algorithm approach underestimates the prevalence of serodiagnosis of syphilis in HIV-infected individuals.

PubMed

Chen, Bin; Peng, Xiuming; Xie, Tiansheng; Jin, Changzhong; Liu, Fumin; Wu, Nanping

2017-07-01

Currently, there are three algorithms for screening of syphilis: traditional algorithm, reverse algorithm and European Centre for Disease Prevention and Control (ECDC) algorithm. To date, there is not a generally recognized diagnostic algorithm. When syphilis meets HIV, the situation is even more complex. To evaluate their screening performance and impact on the seroprevalence of syphilis in HIV-infected individuals, we conducted a cross-sectional study included 865 serum samples from HIV-infected patients in a tertiary hospital. Every sample (one per patient) was tested with toluidine red unheated serum test (TRUST), T. pallidum particle agglutination assay (TPPA), and Treponema pallidum enzyme immunoassay (TP-EIA) according to the manufacturer's instructions. The results of syphilis serological testing were interpreted following different algorithms respectively. We directly compared the traditional syphilis screening algorithm with the reverse syphilis screening algorithm in this unique population. The reverse algorithm achieved remarkable higher seroprevalence of syphilis than the traditional algorithm (24.9% vs. 14.2%, p < 0.0001). Compared to the reverse algorithm, the traditional algorithm also had a missed serodiagnosis rate of 42.8%. The total percentages of agreement and corresponding kappa values of tradition and ECDC algorithm compared with those of reverse algorithm were as follows: 89.4%,0.668; 99.8%, 0.994. There was a very good strength of agreement between the reverse and the ECDC algorithm. Our results supported the reverse (or ECDC) algorithm in screening of syphilis in HIV-infected populations. In addition, our study demonstrated that screening of HIV-populations using different algorithms may result in a statistically different seroprevalence of syphilis.

Diagnostic Utility of the ADI-R and DSM-5 in the Assessment of Latino Children and Adolescents.

PubMed

Magaña, Sandy; Vanegas, Sandra B

2017-05-01

Latino children in the US are systematically underdiagnosed with Autism Spectrum Disorder (ASD); therefore, it is important that recent changes to the diagnostic process do not exacerbate this pattern of under-identification. Previous research has found that the Autism Diagnostic Interview-Revised (ADI-R) algorithm, based on the Diagnostic and Statistical Manual of Mental Disorder, Fourth Edition, Text Revision (DSM-IV-TR), has limitations with Latino children of Spanish speaking parents. We evaluated whether an ADI-R algorithm based on the new DSM-5 classification for ASD would be more sensitive in identifying Latino children of Spanish speaking parents who have a clinical diagnosis of ASD. Findings suggest that the DSM-5 algorithm shows better sensitivity than the DSM-IV-TR algorithm for Latino children.

An algorithm to improve diagnostic accuracy in diabetes in computerised problem orientated medical records (POMR) compared with an established algorithm developed in episode orientated records (EOMR).

PubMed

de Lusignan, Simon; Liaw, Siaw-Teng; Dedman, Daniel; Khunti, Kamlesh; Sadek, Khaled; Jones, Simon

2015-06-05

An algorithm that detects errors in diagnosis, classification or coding of diabetes in primary care computerised medial record (CMR) systems is currently available. However, this was developed on CMR systems that are episode orientated medical records (EOMR); and do not force the user to always code a problem or link data to an existing one. More strictly problem orientated medical record (POMR) systems mandate recording a problem and linking consultation data to them. To compare the rates of detection of diagnostic accuracy using an algorithm developed in EOMR with a new POMR specific algorithm. We used data from The Health Improvement Network (THIN) database (N = 2,466,364) to identify a population of 100,513 (4.08%) patients considered likely to have diabetes. We recalibrated algorithms designed to classify cases of diabetes to take account of that POMR enforced coding consistency in the computerised medical record systems [In Practice Systems (InPS) Vision] that contribute data to THIN. We explored the different proportions of people classified as having type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and with diabetes unclassifiable as either T1DM or T2DM. We compared proportions using chi-square tests and used Tukey's test to compare the characteristics of the people in each group. The prevalence of T1DM using the original EOMR algorithm was 0.38% (9,264/2,466,364), and for T2DM 3.22% (79,417/2,466,364). The prevalence using the new POMR algorithm was 0.31% (7,750/2,466,364) T1DM and 3.65% (89,990/2,466,364) T2DM. The EOMR algorithms also left more people unclassified 11,439 (12%), as to their type of diabetes compared with 2,380 (2.4%), for the new algorithm. Those people who were only classified by the EOMR system differed in terms of older age, and apparently better glycaemic control, despite not being prescribed medication for their diabetes (p < 0.005). Increasing the degree of problem orientation of the medical record system can improve the accuracy of recording of diagnoses and, therefore, the accuracy of using routinely collected data from CMRs to determine the prevalence of diabetes mellitus; data processing strategies should reflect the degree of problem orientation.

Self-Reported HIV-Positive Status But Subsequent HIV-Negative Test Result Using Rapid Diagnostic Testing Algorithms Among Seven Sub-Saharan African Military Populations

DTIC Science & Technology

2017-07-07

RESEARCH ARTICLE Self-reported HIV-positive status but subsequent HIV-negative test result using rapid diagnostic testing algorithms among seven sub...America * judith.harbertson.ctr@mail.mil Abstract HIV rapid diagnostic tests (RDTs) combined in an algorithm are the current standard for HIV diagnosis...in many sub-Saharan African countries, and extensive laboratory testing has con- firmed HIV RDTs have excellent sensitivity and specificity. However

Different CT perfusion algorithms in the detection of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.

PubMed

Cremers, Charlotte H P; Dankbaar, Jan Willem; Vergouwen, Mervyn D I; Vos, Pieter C; Bennink, Edwin; Rinkel, Gabriel J E; Velthuis, Birgitta K; van der Schaaf, Irene C

2015-05-01

Tracer delay-sensitive perfusion algorithms in CT perfusion (CTP) result in an overestimation of the extent of ischemia in thromboembolic stroke. In diagnosing delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH), delayed arrival of contrast due to vasospasm may also overestimate the extent of ischemia. We investigated the diagnostic accuracy of tracer delay-sensitive and tracer delay-insensitive algorithms for detecting DCI. From a prospectively collected series of aSAH patients admitted between 2007-2011, we included patients with any clinical deterioration other than rebleeding within 21 days after SAH who underwent NCCT/CTP/CTA imaging. Causes of clinical deterioration were categorized into DCI and no DCI. CTP maps were calculated with tracer delay-sensitive and tracer delay-insensitive algorithms and were visually assessed for the presence of perfusion deficits by two independent observers with different levels of experience. The diagnostic value of both algorithms was calculated for both observers. Seventy-one patients were included. For the experienced observer, the positive predictive values (PPVs) were 0.67 for the delay-sensitive and 0.66 for the delay-insensitive algorithm, and the negative predictive values (NPVs) were 0.73 and 0.74. For the less experienced observer, PPVs were 0.60 for both algorithms, and NPVs were 0.66 for the delay-sensitive and 0.63 for the delay-insensitive algorithm. Test characteristics are comparable for tracer delay-sensitive and tracer delay-insensitive algorithms for the visual assessment of CTP in diagnosing DCI. This indicates that both algorithms can be used for this purpose.

Code-based Diagnostic Algorithms for Idiopathic Pulmonary Fibrosis. Case Validation and Improvement.

PubMed

Ley, Brett; Urbania, Thomas; Husson, Gail; Vittinghoff, Eric; Brush, David R; Eisner, Mark D; Iribarren, Carlos; Collard, Harold R

2017-06-01

Population-based studies of idiopathic pulmonary fibrosis (IPF) in the United States have been limited by reliance on diagnostic code-based algorithms that lack clinical validation. To validate a well-accepted International Classification of Diseases, Ninth Revision, code-based algorithm for IPF using patient-level information and to develop a modified algorithm for IPF with enhanced predictive value. The traditional IPF algorithm was used to identify potential cases of IPF in the Kaiser Permanente Northern California adult population from 2000 to 2014. Incidence and prevalence were determined overall and by age, sex, and race/ethnicity. A validation subset of cases (n = 150) underwent expert medical record and chest computed tomography review. A modified IPF algorithm was then derived and validated to optimize positive predictive value. From 2000 to 2014, the traditional IPF algorithm identified 2,608 cases among 5,389,627 at-risk adults in the Kaiser Permanente Northern California population. Annual incidence was 6.8/100,000 person-years (95% confidence interval [CI], 6.1-7.7) and was higher in patients with older age, male sex, and white race. The positive predictive value of the IPF algorithm was only 42.2% (95% CI, 30.6 to 54.6%); sensitivity was 55.6% (95% CI, 21.2 to 86.3%). The corrected incidence was estimated at 5.6/100,000 person-years (95% CI, 2.6-10.3). A modified IPF algorithm had improved positive predictive value but reduced sensitivity compared with the traditional algorithm. A well-accepted International Classification of Diseases, Ninth Revision, code-based IPF algorithm performs poorly, falsely classifying many non-IPF cases as IPF and missing a substantial proportion of IPF cases. A modification of the IPF algorithm may be useful for future population-based studies of IPF.

Comparison of Traditional and Reverse Syphilis Screening Algorithms in Medical Health Checkups.

PubMed

Nah, Eun Hee; Cho, Seon; Kim, Suyoung; Cho, Han Ik; Chai, Jong Yil

2017-11-01

The syphilis diagnostic algorithms applied in different countries vary significantly depending on the local syphilis epidemiology and other considerations, including the expected workload, the need for automation in the laboratory and budget factors. This study was performed to investigate the efficacy of traditional and reverse syphilis diagnostic algorithms during general health checkups. In total, 1,000 blood specimens were obtained from 908 men and 92 women during their regular health checkups. Traditional screening and reverse screening were applied to the same specimens using automatic rapid plasma regain (RPR) and Treponema pallidum latex agglutination (TPLA) tests, respectively. Specimens that were reverse algorithm (TPLA) reactive, were subjected to a second treponemal test performed by using the chemiluminescent microparticle immunoassay (CMIA). Of the 1,000 specimens tested, 68 (6.8%) were reactive by reverse screening (TPLA) compared with 11 (1.1%) by traditional screening (RPR). The traditional algorithm failed to detect 48 specimens [TPLA(+)/RPR(-)/CMIA(+)]. The median TPLA cutoff index (COI) was higher in CMIA-reactive cases than in CMIA-nonreactive cases (90.5 vs 12.5 U). The reverse screening algorithm could detect the subjects with possible latent syphilis who were not detected by the traditional algorithm. Those individuals could be provided with opportunities for evaluating syphilis during their health checkups. The COI values of the initial TPLA test may be helpful in excluding false-positive TPLA test results in the reverse algorithm. © The Korean Society for Laboratory Medicine

Diagnostic accuracy of contrast-enhanced ultrasound for the differential diagnosis of hepatocellular carcinoma: ESCULAP versus CEUS-LI-RADS.

PubMed

Schellhaas, Barbara; Görtz, Ruediger S; Pfeifer, Lukas; Kielisch, Christian; Neurath, Markus F; Strobel, Deike

2017-09-01

A comparison is made of two contrast-enhanced ultrasound (CEUS) algorithms for the diagnosis of hepatocellular carcinoma (HCC) in high-risk patients: Erlanger Synopsis of Contrast-enhanced Ultrasound for Liver lesion Assessment in Patients at Risk (ESCULAP) and American College of Radiology Contrast-Enhanced Ultrasound-Liver Imaging Reporting and Data System (ACR-CEUS-LI-RADSv.2016). Focal liver lesions in 100 high-risk patients were assessed using both CEUS algorithms (ESCULAP and CEUS-LI-RADSv.2016) for a direct comparison. Lesions were categorized according to size and contrast enhancement in the arterial, portal venous and late phases.For the definite diagnosis of HCC, categories ESCULAP-4, ESCULAP-Tr and ESCULAP-V and CEUS-LI-RADS-LR-5, LR-Tr and LR-5-V were compared. In addition, CEUS-LI-RADS-category LR-M (definitely/probably malignant, but not specific for HCC) and ESCULAP-category C [intrahepatic cholangiocellular carcinoma (ICC)] were compared.Histology, CE-computed tomography and CE-MRI served as reference standards. The reference standard among 100 lesions included 87 HCCs, six ICCs and seven non-HCC-non-ICC-lesions. For the diagnosis of HCC, the diagnostic accuracy of CEUS was significantly higher with ESCULAP versus CEUS-LI-RADS (94.3%/72.4%; p<0.01). Sensitivity, specificity and positive predictive value (PPV) and negative predictive value for ESCULAP/CEUS-LI-RADS were 94.3%/72.4%; 61.5%/69.2%; 94.3%/94%; and 61.5%/27.3%, respectively.The diagnostic accuracy for ICC (LR-M/ESCULAP-C) was identical with both algorithms (50%), with higher PPV for ESCULAP-C versus LR-M (75 vs. 50%). CEUS-based algorithms contribute toward standardized assessment and reporting of HCC-suspect lesions in high-risk patients. ESCULAP shows significantly higher diagnostic accuracy, sensitivity and negative predictive value with no loss of specificity compared with CEUS-LI-RADS. Both algorithms have an excellent PPV. Arterial hyperenhancement is the key feature for the diagnosis of HCC with CEUS. Washout should not be a necessary prerequisite for the diagnosis of definite HCC. CEUS-LI-RADS in its current version is inferior to ESCULAP for the noninvasive diagnosis of HCC. There are two ways to improve CEUS-LI-RADS: firstly, combination of the categories LR-4 and LR-5 for the diagnosis of definite HCC, and secondly, use of subtotal infiltration of a liver lobe as an additional feature.

A real-time spectral mapper as an emerging diagnostic technology in biomedical sciences.

PubMed

Epitropou, George; Kavvadias, Vassilis; Iliou, Dimitris; Stathopoulos, Efstathios; Balas, Costas

2013-01-01

Real time spectral imaging and mapping at video rates can have tremendous impact not only on diagnostic sciences but also on fundamental physiological problems. We report the first real-time spectral mapper based on the combination of snap-shot spectral imaging and spectral estimation algorithms. Performance evaluation revealed that six band imaging combined with the Wiener algorithm provided high estimation accuracy, with error levels lying within the experimental noise. High accuracy is accompanied with much faster, by 3 orders of magnitude, spectral mapping, as compared with scanning spectral systems. This new technology is intended to enable spectral mapping at nearly video rates in all kinds of dynamic bio-optical effects as well as in applications where the target-probe relative position is randomly and fast changing.

Diagnosis and treatment of gastroesophageal reflux disease complicated by Barrett's esophagus.

PubMed

Stasyshyn, Andriy

2017-08-31

The aim of the study was to evaluate the effectiveness of a diagnostic and therapeutic algorithm for gastroesophageal reflux disease complicated by Barrett's esophagus in 46 patients. A diagnostic and therapeutic algorithm for complicated GERD was developed. To describe the changes in the esophagus with reflux esophagitis, the Los Angeles classification was used. Intestinal metaplasia of the epithelium in the lower third of the esophagus was assessed using videoendoscopy, chromoscopy, and biopsy. Quality of life was assessed with the Gastro-Intestinal Quality of Life Index. The used methods were modeling, clinical, analytical, comparative, standardized, and questionnaire-based. Results and their discussion. Among the complications of GERD, Barrett's esophagus was diagnosed in 9 (19.6 %), peptic ulcer in the esophagus in 10 (21.7 %), peptic stricture of the esophagus in 4 (8.7 %), esophageal-gastric bleeding in 23 (50.0 %), including Malory-Weiss syndrome in 18, and erosive ulcerous bleeding in 5 people. Hiatal hernia was diagnosed in 171 (87.7 %) patients (sliding in 157 (91.8%), paraesophageal hernia in 2 (1.2%), and mixed hernia in 12 (7.0%) cases). One hundred ninety-five patients underwent laparoscopic surgery. Nissen fundoplication was conducted in 176 (90.2%) patients, Toupet fundoplication in 14 (7.2%), and Dor fundoplication in 5 (2.6%). It was established that the use of the diagnostic and treatment algorithm promoted systematization and objectification of changes in complicated GERD, contributed to early diagnosis, helped in choosing treatment, and improved quality of life. Argon coagulation and use of PPIs for 8-12 weeks before surgery led to the regeneration of the mucous membrane in the esophagus. The developed diagnostic and therapeutic algorithm facilitated systematization and objectification of changes in complicated GERD, contributed to early diagnosis, helped in choosing treatment, and improved quality of life.

Diagnostic algorithm for relapsing acquired demyelinating syndromes in children.

PubMed

Hacohen, Yael; Mankad, Kshitij; Chong, W K; Barkhof, Frederik; Vincent, Angela; Lim, Ming; Wassmer, Evangeline; Ciccarelli, Olga; Hemingway, Cheryl

2017-07-18

To establish whether children with relapsing acquired demyelinating syndromes (RDS) and myelin oligodendrocyte glycoprotein antibodies (MOG-Ab) show distinctive clinical and radiologic features and to generate a diagnostic algorithm for the main RDS for clinical use. A panel reviewed the clinical characteristics, MOG-Ab and aquaporin-4 (AQP4) Ab, intrathecal oligoclonal bands, and Epstein-Barr virus serology results of 110 children with RDS. A neuroradiologist blinded to the diagnosis scored the MRI scans. Clinical, radiologic, and serologic tests results were compared. The findings showed that 56.4% of children were diagnosed with multiple sclerosis (MS), 25.4% with neuromyelitis optica spectrum disorder (NMOSD), 12.7% with multiphasic disseminated encephalomyelitis (MDEM), and 5.5% with relapsing optic neuritis (RON). Blinded analysis defined baseline MRI as typical of MS in 93.5% of children with MS. Acute disseminated encephalomyelitis presentation was seen only in the non-MS group. Of NMOSD cases, 30.7% were AQP4-Ab positive. MOG-Ab were found in 83.3% of AQP4-Ab-negative NMOSD, 100% of MDEM, and 33.3% of RON. Children with MOG-Ab were younger, were less likely to present with area postrema syndrome, and had lower disability, longer time to relapse, and more cerebellar peduncle lesions than children with AQP4-Ab NMOSD. A diagnostic algorithm applicable to any episode of CNS demyelination leads to 4 main phenotypes: MS, AQP4-Ab NMOSD, MOG-Ab-associated disease, and antibody-negative RDS. Children with MS and AQP4-Ab NMOSD showed features typical of adult cases. Because MOG-Ab-positive children showed notable and distinctive clinical and MRI features, they were grouped into a unified phenotype (MOG-Ab-associated disease), included in a new diagnostic algorithm. © 2017 American Academy of Neurology.

[Diagnostic performance of biliary ultrasound vs. magnetic resonance cholangiogram in patients with recurrent biliary obstruction.].

PubMed

Chávez-Valencia, V; Espinosa-Ortega, H F; Espinoza-Peralta, D; Arce-Salinas, C A

2009-01-01

Obstructive jaundice in patients with previous cholecystectomy requires a precise diagnosis. In the diagnostic algorithm, biliary ultrasound (BUS) and magnetic resonance cholangiogram (MRC) are used, although the accuracy of each method is unknown in our setting. No previous comparison of US and MRC in subjects with cholecystectomy has been made. To determine diagnostic accuracy of BUS and MRC in patients with recurrent biliary obstruction. Patients with endoscopic retrograde cholangiopacreatography (ERCP) demonstrating recurrent biliary obstruction by stones were included. All patients underwent BUS and MRC. We determined the diagnostic performance of each image study compared with ERCP. Twenty-seven patients with a mean age of 62.9 +/- 17.3 years-old were included. Sensitivity and specificity of BUS were 0.12 and 0.58, respectively. Figures for MRC were 0.88 and 0.82. Diagnostic agreement between ERCP and MRC was k= 0.66 whereas BUS had a k of only 0.26. MRC had good diagnostic performance for recurrent choledocolithiasis. BUS demonstrated lower accuracy compared with previous reports, so should not be considered in the initial approach of recurrent choledocus obstruction.

Statistical physics of medical diagnostics: Study of a probabilistic model.

PubMed

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Statistical physics of medical diagnostics: Study of a probabilistic model

NASA Astrophysics Data System (ADS)

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Application of content-based image compression to telepathology

NASA Astrophysics Data System (ADS)

Varga, Margaret J.; Ducksbury, Paul G.; Callagy, Grace

2002-05-01

Telepathology is a means of practicing pathology at a distance, viewing images on a computer display rather than directly through a microscope. Without compression, images take too long to transmit to a remote location and are very expensive to store for future examination. However, to date the use of compressed images in pathology remains controversial. This is because commercial image compression algorithms such as JPEG achieve data compression without knowledge of the diagnostic content. Often images are lossily compressed at the expense of corrupting informative content. None of the currently available lossy compression techniques are concerned with what information has been preserved and what data has been discarded. Their sole objective is to compress and transmit the images as fast as possible. By contrast, this paper presents a novel image compression technique, which exploits knowledge of the slide diagnostic content. This 'content based' approach combines visually lossless and lossy compression techniques, judiciously applying each in the appropriate context across an image so as to maintain 'diagnostic' information while still maximising the possible compression. Standard compression algorithms, e.g. wavelets, can still be used, but their use in a context sensitive manner can offer high compression ratios and preservation of diagnostically important information. When compared with lossless compression the novel content-based approach can potentially provide the same degree of information with a smaller amount of data. When compared with lossy compression it can provide more information for a given amount of compression. The precise gain in the compression performance depends on the application (e.g. database archive or second opinion consultation) and the diagnostic content of the images.

Assessment of the patient, health system, and population effects of Xpert MTB/RIF and alternative diagnostics for tuberculosis in Tanzania: an integrated modelling approach.

PubMed

Langley, Ivor; Lin, Hsien-Ho; Egwaga, Saidi; Doulla, Basra; Ku, Chu-Chang; Murray, Megan; Cohen, Ted; Squire, S Bertel

2014-10-01

Several promising new diagnostic methods and algorithms for tuberculosis have been endorsed by WHO. National tuberculosis programmes now face the decision on which methods to implement and where to place them in the diagnostic algorithm. We used an integrated model to assess the effects of different algorithms of Xpert MTB/RIF and light-emitting diode (LED) fluorescence microscopy in Tanzania. To understand the effects of new diagnostics from the patient, health system, and population perspective, the model incorporated and linked a detailed operational component and a transmission component. The model was designed to represent the operational and epidemiological context of Tanzania and was used to compare the effects and cost-effectiveness of different diagnostic options. Among the diagnostic options considered, we identified three strategies as cost effective in Tanzania. Full scale-up of Xpert would have the greatest population-level effect with the highest incremental cost: 346 000 disability-adjusted life-years (DALYs) averted with an additional cost of US$36·9 million over 10 years. The incremental cost-effectiveness ratio (ICER) of Xpert scale-up ($169 per DALY averted, 95% credible interval [CrI] 104-265) is below the willingness-to-pay threshold ($599) for Tanzania. Same-day LED fluorescence microscopy is the next most effective strategy with an ICER of $45 (95% CrI 25-74), followed by LED fluorescence microscopy with an ICER of $29 (6-59). Compared with same-day LED fluorescence microscopy and Xpert full rollout, targeted use of Xpert in presumptive tuberculosis cases with HIV infection, either as an initial diagnostic test or as a follow-on test to microscopy, would produce DALY gains at a higher incremental cost and therefore is dominated in the context of Tanzania. For Tanzania, this integrated modelling approach predicts that full rollout of Xpert is a cost-effective option for tuberculosis diagnosis and has the potential to substantially reduce the national tuberculosis burden. It also estimates the substantial level of funding that will need to be mobilised to translate this into clinical practice. This approach could be adapted and replicated in other developing countries to inform rational health policy formulation. Copyright © 2014 Langley et al. Open Access article distributed under the terms of CC BY-NC-SA. Published by .. All rights reserved.

Strategies to improve the efficiency of celiac disease diagnosis in the laboratory.

PubMed

González, Delia Almeida; de Armas, Laura García; Rodríguez, Itahisa Marcelino; Almeida, Ana Arencibia; García, Miriam García; Gannar, Fadoua; de León, Antonio Cabrera

2017-10-01

The demand for testing to detect celiac disease (CD) autoantibodies has increased, together with the cost per case diagnosed, resulting in the adoption of measures to restrict laboratory testing. We designed this study to determine whether opportunistic screening to detect CD-associated autoantibodies had advantages compared to efforts to restrict testing, and to identify the most cost-effective diagnostic strategy. We compared a group of 1678 patients in which autoantibody testing was restricted to cases in which the test referral was considered appropriate (G1) to a group of 2140 patients in which test referrals were not reviewed or restricted (G2). Two algorithms A (quantifying IgA and Tissue transglutaminase IgA [TG-IgA] in all patients), and B (quantifying only TG-IgA in all patients) were used in each group, and the cost-effectiveness of each strategy was calculated. TG-IgA autoantibodies were positive in 62 G1 patients and 69 G2 patients. Among those positive for tissue transglutaminase IgA and endomysial IgA autoantibodies, the proportion of patients with de novo autoantibodies was lower (p=0.028) in G1 (11/62) than in G2 (24/69). Algorithm B required fewer determinations than algorithm A in both G1 (2310 vs 3493; p<0.001) and G2 (2196 vs 4435; p<0.001). With algorithm B the proportion of patients in whom IgA was tested was lower (p<0.001) in G2 (29/2140) than in G1 (617/1678). The lowest cost per case diagnosed (4.63 euros/patient) was found with algorithm B in G2. We conclude that opportunistic screening has advantages compared to efforts in the laboratory to restrict CD diagnostic testing. The most cost-effective strategy was based on the use of an appropriate algorithm. Copyright © 2017. Published by Elsevier B.V.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

The performance of the SEPT9 gene methylation assay and a comparison with other CRC screening tests: A meta-analysis.

PubMed

Song, Lele; Jia, Jia; Peng, Xiumei; Xiao, Wenhua; Li, Yuemin

2017-06-08

The SEPT9 gene methylation assay is the first FDA-approved blood assay for colorectal cancer (CRC) screening. Fecal immunochemical test (FIT), FIT-DNA test and CEA assay are also in vitro diagnostic (IVD) tests used in CRC screening. This meta-analysis aims to review the SEPT9 assay performance and compare it with other IVD CRC screening tests. By searching the Ovid MEDLINE, EMBASE, CBMdisc and CJFD database, 25 out of 180 studies were identified to report the SEPT9 assay performance. 2613 CRC cases and 6030 controls were included, and sensitivity and specificity were used to evaluate its performance at various algorithms. 1/3 algorithm exhibited the best sensitivity while 2/3 and 1/1 algorithm exhibited the best balance between sensitivity and specificity. The performance of the blood SEPT9 assay is superior to that of the serum protein markers and the FIT test in symptomatic population, while appeared to be less potent than FIT and FIT-DNA tests in asymptomatic population. In conclusion, 1/3 algorithm is recommended for CRC screening, and 2/3 or 1/1 algorithms are suitable for early detection for diagnostic purpose. The SEPT9 assay exhibited better performance in symptomatic population than in asymptomatic population.

Integrating Oil Debris and Vibration Measurements for Intelligent Machine Health Monitoring. Degree awarded by Toledo Univ., May 2002

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.

2003-01-01

A diagnostic tool for detecting damage to gears was developed. Two different measurement technologies, oil debris analysis and vibration were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig. An oil debris sensor and the two vibration algorithms were adapted as the diagnostic tools. An inductance type oil debris sensor was selected for the oil analysis measurement technology. Gear damage data for this type of sensor was limited to data collected in the NASA Glenn test rigs. For this reason, this analysis included development of a parameter for detecting gear pitting damage using this type of sensor. The vibration data was used to calculate two previously available gear vibration diagnostic algorithms. The two vibration algorithms were selected based on their maturity and published success in detecting damage to gears. Oil debris and vibration features were then developed using fuzzy logic analysis techniques, then input into a multi sensor data fusion process. Results show combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spur gears. As a result of this research, this new diagnostic tool has significantly improved detection of gear damage in the NASA Glenn Spur Gear Fatigue Rigs. This research also resulted in several other findings that will improve the development of future health monitoring systems. Oil debris analysis was found to be more reliable than vibration analysis for detecting pitting fatigue failure of gears and is capable of indicating damage progression. Also, some vibration algorithms are as sensitive to operational effects as they are to damage. Another finding was that clear threshold limits must be established for diagnostic tools. Based on additional experimental data obtained from the NASA Glenn Spiral Bevel Gear Fatigue Rig, the methodology developed in this study can be successfully implemented on other geared systems.

[Cost-effectiveness of the deep vein thrombosis diagnosis process in primary care].

PubMed

Fuentes Camps, Eva; Luis del Val García, José; Bellmunt Montoya, Sergi; Hmimina Hmimina, Sara; Gómez Jabalera, Efren; Muñoz Pérez, Miguel Ángel

2016-04-01

To analyse the cost effectiveness of the application of diagnostic algorithms in patients with a first episode of suspected deep vein thrombosis (DVT) in Primary Care compared with systematic referral to specialised centres. Observational, cross-sectional, analytical study. Patients from hospital emergency rooms referred from Primary Care to complete clinical evaluation and diagnosis. A total of 138 patients with symptoms of a first episode of DVT were recruited; 22 were excluded (no Primary Care report, symptoms for more than 30 days, anticoagulant treatment, and previous DVT). Of the 116 patients finally included, 61% women and the mean age was 71 years. Variables from the Wells and Oudega clinical probability scales, D-dimer (portable and hospital), Doppler ultrasound, and direct costs generated by the three algorithms analysed: all patients were referred systematically, referral according to Wells and Oudega scale. DVT was confirmed in 18.9%. The two clinical probability scales showed a sensitivity of 100% (95% CI: 85.1 to 100) and a specificity of about 40%. With the application of the scales, one third of all referrals to hospital emergency rooms could have been avoided (P<.001). The diagnostic cost could have been reduced by € 8,620 according to Oudega and € 9,741 according to Wells, per 100 patients visited. The application of diagnostic algorithms when a DVT is suspected could lead to better diagnostic management by physicians, and a more cost effective process. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

TH-A-BRF-11: Image Intensity Non-Uniformities Between MRI Simulation and Diagnostic MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paulson, E

2014-06-15

Purpose: MRI simulation for MRI-based radiotherapy demands that patients be setup in treatment position, which frequently involves use of alternative radiofrequency (RF) coil configurations to accommodate immobilized patients. However, alternative RF coil geometries may exacerbate image intensity non-uniformities (IINU) beyond those observed in diagnostic MRI, which may challenge image segmentation and registration accuracy as well as confound studies assessing radiotherapy response when MR simulation images are used as baselines for evaluation. The goal of this work was to determine whether differences in IINU exist between MR simulation and diagnostic MR images. Methods: ACR-MRI phantom images were acquired at 3T usingmore » a spin-echo sequence (TE/TR:20/500ms, rBW:62.5kHz, TH/skip:5/5mm). MR simulation images were obtained by wrapping two flexible phased-array RF coils around the phantom. Diagnostic MR images were obtained by placing the phantom into a commercial phased-array head coil. Pre-scan normalization was enabled in both cases. Images were transferred offline and corrected for IINU using the MNI N3 algorithm. Coefficients of variation (CV=σ/μ) were calculated for each slice. Wilcoxon matched-pairs and Mann-Whitney tests compared CV values between original and N3 images and between MR simulation and diagnostic MR images. Results: Significant differences in CV were detected between original and N3 images in both MRI simulation and diagnostic MRI groups (p=0.010, p=0.010). In addition, significant differences in CV were detected between original MR simulation and original and N3 diagnostic MR images (p=0.0256, p=0.0016). However, no significant differences in CV were detected between N3 MR simulation images and original or N3 diagnostic MR images, demonstrating the importance of correcting MR simulation images beyond pre-scan normalization prior to use in radiotherapy. Conclusions: Alternative RF coil configurations used in MRI simulation can Result in significant IINU differences compared to diagnostic MR images. The MNI N3 algorithm reduced MR simulation IINU to levels observed in diagnostic MR images. Funding provided by Advancing a Healthier Wisconsin.« less

Portable Health Algorithms Test System

NASA Technical Reports Server (NTRS)

Melcher, Kevin J.; Wong, Edmond; Fulton, Christopher E.; Sowers, Thomas S.; Maul, William A.

2010-01-01

A document discusses the Portable Health Algorithms Test (PHALT) System, which has been designed as a means for evolving the maturity and credibility of algorithms developed to assess the health of aerospace systems. Comprising an integrated hardware-software environment, the PHALT system allows systems health management algorithms to be developed in a graphical programming environment, to be tested and refined using system simulation or test data playback, and to be evaluated in a real-time hardware-in-the-loop mode with a live test article. The integrated hardware and software development environment provides a seamless transition from algorithm development to real-time implementation. The portability of the hardware makes it quick and easy to transport between test facilities. This hard ware/software architecture is flexible enough to support a variety of diagnostic applications and test hardware, and the GUI-based rapid prototyping capability is sufficient to support development execution, and testing of custom diagnostic algorithms. The PHALT operating system supports execution of diagnostic algorithms under real-time constraints. PHALT can perform real-time capture and playback of test rig data with the ability to augment/ modify the data stream (e.g. inject simulated faults). It performs algorithm testing using a variety of data input sources, including real-time data acquisition, test data playback, and system simulations, and also provides system feedback to evaluate closed-loop diagnostic response and mitigation control.

Stationary intraoral tomosynthesis for dental imaging

NASA Astrophysics Data System (ADS)

Inscoe, Christina R.; Wu, Gongting; Soulioti, Danai E.; Platin, Enrique; Mol, Andre; Gaalaas, Laurence R.; Anderson, Michael R.; Tucker, Andrew W.; Boyce, Sarah; Shan, Jing; Gonzales, Brian; Lu, Jianping; Zhou, Otto

2017-03-01

Despite recent advances in dental radiography, the diagnostic accuracies for some of the most common dental diseases have not improved significantly, and in some cases remain low. Intraoral x-ray is the most commonly used x-ray diagnostic tool in dental clinics. It however suffers from the typical limitations of a 2D imaging modality including structure overlap. Cone-beam computed tomography (CBCT) uses high radiation dose and suffers from image artifacts and relatively low resolution. The purpose of this study is to investigate the feasibility of developing a stationary intraoral tomosynthesis (s-IOT) using spatially distributed carbon nanotube (CNT) x-ray array technology, and to evaluate its diagnostic accuracy compared to conventional 2D intraoral x-ray. A bench-top s-IOT device was constructed using a linear CNT based X-ray source array and a digital intraoral detector. Image reconstruction was performed using an iterative reconstruction algorithm. Studies were performed to optimize the imaging configuration. For evaluation of s-IOT's diagnostic accuracy, images of a dental quality assurance phantom, and extracted human tooth specimens were acquired. Results show s-IOT increases the diagnostic sensitivity for caries compared to intraoral x-ray at a comparable dose level.

A novel computer-assisted image analysis of [123I]β-CIT SPECT images improves the diagnostic accuracy of parkinsonian disorders.

PubMed

Goebel, Georg; Seppi, Klaus; Donnemiller, Eveline; Warwitz, Boris; Wenning, Gregor K; Virgolini, Irene; Poewe, Werner; Scherfler, Christoph

2011-04-01

The purpose of this study was to develop an observer-independent algorithm for the correct classification of dopamine transporter SPECT images as Parkinson's disease (PD), multiple system atrophy parkinson variant (MSA-P), progressive supranuclear palsy (PSP) or normal. A total of 60 subjects with clinically probable PD (n = 15), MSA-P (n = 15) and PSP (n = 15), and 15 age-matched healthy volunteers, were studied with the dopamine transporter ligand [(123)I]β-CIT. Parametric images of the specific-to-nondisplaceable equilibrium partition coefficient (BP(ND)) were generated. Following a voxel-wise ANOVA, cut-off values were calculated from the voxel values of the resulting six post-hoc t-test maps. The percentages of the volume of an individual BP(ND) image remaining below and above the cut-off values were determined. The higher percentage of image volume from all six cut-off matrices was used to classify an individual's image. For validation, the algorithm was compared to a conventional region of interest analysis. The predictive diagnostic accuracy of the algorithm in the correct assignment of a [(123)I]β-CIT SPECT image was 83.3% and increased to 93.3% on merging the MSA-P and PSP groups. In contrast the multinomial logistic regression of mean region of interest values of the caudate, putamen and midbrain revealed a diagnostic accuracy of 71.7%. In contrast to a rater-driven approach, this novel method was superior in classifying [(123)I]β-CIT-SPECT images as one of four diagnostic entities. In combination with the investigator-driven visual assessment of SPECT images, this clinical decision support tool would help to improve the diagnostic yield of [(123)I]β-CIT SPECT in patients presenting with parkinsonism at their initial visit.

Diagnostic Accuracy of the Veteran Affairs' Traumatic Brain Injury Screen.

PubMed

Louise Bender Pape, Theresa; Smith, Bridget; Babcock-Parziale, Judith; Evans, Charlesnika T; Herrold, Amy A; Phipps Maieritsch, Kelly; High, Walter M

2018-01-31

To comprehensively estimate the diagnostic accuracy and reliability of the Department of Veterans Affairs (VA) Traumatic Brain Injury (TBI) Clinical Reminder Screen (TCRS). Cross-sectional, prospective, observational study using the Standards for Reporting of Diagnostic Accuracy criteria. Three VA Polytrauma Network Sites. Operation Iraqi Freedom, Operation Enduring Freedom veterans (N=433). TCRS, Comprehensive TBI Evaluation, Structured TBI Diagnostic Interview, Symptom Attribution and Classification Algorithm, and Clinician-Administered Posttraumatic Stress Disorder (PTSD) Scale. Forty-five percent of veterans screened positive on the TCRS for TBI. For detecting occurrence of historical TBI, the TCRS had a sensitivity of .56 to .74, a specificity of .63 to .93, a positive predictive value (PPV) of 25% to 45%, a negative predictive value (NPV) of 91% to 94%, and a diagnostic odds ratio (DOR) of 4 to 13. For accuracy of attributing active symptoms to the TBI, the TCRS had a sensitivity of .64 to .87, a specificity of .59 to .89, a PPV of 26% to 32%, an NPV of 92% to 95%, and a DOR of 6 to 9. The sensitivity was higher for veterans with PTSD (.80-.86) relative to veterans without PTSD (.57-.82). The specificity, however, was higher among veterans without PTSD (.75-.81) relative to veterans with PTSD (.36-.49). All indices of diagnostic accuracy changed when participants with questionably valid (QV) test profiles were eliminated from analyses. The utility of the TCRS to screen for mild TBI (mTBI) depends on the stringency of the diagnostic reference standard to which it is being compared, the presence/absence of PTSD, and QV test profiles. Further development, validation, and use of reproducible diagnostic algorithms for symptom attribution after possible mTBI would improve diagnostic accuracy. Published by Elsevier Inc.

Image quality enhancement for skin cancer optical diagnostics

NASA Astrophysics Data System (ADS)

Bliznuks, Dmitrijs; Kuzmina, Ilona; Bolocko, Katrina; Lihachev, Alexey

2017-12-01

The research presents image quality analysis and enhancement proposals in biophotonic area. The sources of image problems are reviewed and analyzed. The problems with most impact in biophotonic area are analyzed in terms of specific biophotonic task - skin cancer diagnostics. The results point out that main problem for skin cancer analysis is the skin illumination problems. Since it is often not possible to prevent illumination problems, the paper proposes image post processing algorithm - low frequency filtering. Practical results show diagnostic results improvement after using proposed filter. Along that, filter do not reduces diagnostic results' quality for images without illumination defects. Current filtering algorithm requires empirical tuning of filter parameters. Further work needed to test the algorithm in other biophotonic applications and propose automatic filter parameter selection.

Document-Level Classification of CT Pulmonary Angiography Reports based on an Extension of the ConText Algorithm

PubMed Central

Chapman, Brian E.; Lee, Sean; Kang, Hyunseok Peter; Chapman, Wendy W.

2011-01-01

In this paper we describe an application called peFinder for document-level classification of CT pulmonary angiography reports. peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents. peFinder was used to answer questions about the disease state (pulmonary emboli present or absent), the certainty state of the diagnosis (uncertainty present or absent), the temporal state of an identified pulmonary embolus (acute or chronic), and the technical quality state of the exam (diagnostic or not diagnostic). Gold standard answers for each question were determined from the consensus classifications of three human annotators. peFinder results were compared to naive Bayes’ classifiers using unigrams and bigrams. The sensitivities (and positive predictive values) for peFinder were 0.98(0.83), 0.86(0.96), 0.94(0.93), and 0.60(0.90) for disease state, quality state, certainty state, and temporal state respectively, compared to 0.68(0.77), 0.67(0.87), 0.62(0.82), and 0.04(0.25) for the naive Bayes’ classifier using unigrams, and 0.75(0.79), 0.52(0.69), 0.59(0.84), and 0.04(0.25) for the naive Bayes’ classifier using bigrams. PMID:21459155

Evidence-Based Diagnostic Algorithm for Glioma: Analysis of the Results of Pathology Panel Review and Molecular Parameters of EORTC 26951 and 26882 Trials.

PubMed

Kros, Johan M; Huizer, Karin; Hernández-Laín, Aurelio; Marucci, Gianluca; Michotte, Alex; Pollo, Bianca; Rushing, Elisabeth J; Ribalta, Teresa; French, Pim; Jaminé, David; Bekka, Nawal; Lacombe, Denis; van den Bent, Martin J; Gorlia, Thierry

2015-06-10

With the rapid discovery of prognostic and predictive molecular parameters for glioma, the status of histopathology in the diagnostic process should be scrutinized. Our project aimed to construct a diagnostic algorithm for gliomas based on molecular and histologic parameters with independent prognostic values. The pathology slides of 636 patients with gliomas who had been included in EORTC 26951 and 26882 trials were reviewed using virtual microscopy by a panel of six neuropathologists who independently scored 18 histologic features and provided an overall diagnosis. The molecular data for IDH1, 1p/19q loss, EGFR amplification, loss of chromosome 10 and chromosome arm 10q, gain of chromosome 7, and hypermethylation of the promoter of MGMT were available for some of the cases. The slides were divided in discovery (n = 426) and validation sets (n = 210). The diagnostic algorithm resulting from analysis of the discovery set was validated in the latter. In 66% of cases, consensus of overall diagnosis was present. A diagnostic algorithm consisting of two molecular markers and one consensus histologic feature was created by conditional inference tree analysis. The order of prognostic significance was: 1p/19q loss, EGFR amplification, and astrocytic morphology, which resulted in the identification of four diagnostic nodes. Validation of the nodes in the validation set confirmed the prognostic value (P < .001). We succeeded in the creation of a timely diagnostic algorithm for anaplastic glioma based on multivariable analysis of consensus histopathology and molecular parameters. © 2015 by American Society of Clinical Oncology.

The Autism Diagnostic Observation Schedule, Module 4: Application of the Revised Algorithms in an Independent, Well-Defined, Dutch Sample (N = 93)

ERIC Educational Resources Information Center

de Bildt, Annelies; Sytema, Sjoerd; Meffert, Harma; Bastiaansen, Jojanneke A. C. J.

2016-01-01

This study examined the discriminative ability of the revised Autism Diagnostic Observation Schedule module 4 algorithm (Hus and Lord in "J Autism Dev Disord" 44(8):1996-2012, 2014) in 93 Dutch males with Autism Spectrum Disorder (ASD), schizophrenia, psychopathy or controls. Discriminative ability of the revised algorithm ASD cut-off…

The Accuracy of Diagnostic Tests for Lyme Disease in Humans, A Systematic Review and Meta-Analysis of North American Research.

PubMed

Waddell, Lisa A; Greig, Judy; Mascarenhas, Mariola; Harding, Shannon; Lindsay, Robbin; Ogden, Nicholas

2016-01-01

There has been an increasing incidence of Lyme disease (LD) in Canada and the United States corresponding to the expanding range of the Ixodes tick vector and Lyme disease agent (Borrelia burgdorferi sensu stricto). There are many diagnostic tests for LD available in North America, all of which have some performance issues, and physicians are concerned about the appropriate use and interpretation of these tests. The objective of this systematic review is to summarize the North American evidence on the accuracy of diagnostic tests and test regimes at various stages of LD. Included in the review are 48 studies on diagnostic tests used in North America published since 1995. Thirteen studies examined a two-tier serological test protocol vs. clinical diagnosis, 24 studies examined single assays vs. clinical diagnosis, 9 studies examined single immunoblot vs. clinical diagnosis, 7 studies compared culture or PCR direct detection methods vs. clinical diagnosis, 22 studies compared two or more tests with each other and 8 studies compared a two-tiered serological test protocol to another test. Recent studies examining the sensitivity and specificity of various test protocols noted that the Immunetics® C6 B. burgdorferi ELISA™ and the two tier approach have superior specificity compared to proposed replacements, and the CDC recommended western blot algorithm has equivalent or superior specificity over other proposed test algorithms. There is a dramatic increase in test sensitivity with progression of B. burgdorferi infection from early to late LD. Direct detection methods, culture and PCR of tissue or blood samples were not as sensitive or timely compared to serological testing. It was also noted that there are a large number of both commercial (n = 42) and in-house developed tests used by private laboratories which have not been evaluated in the primary literature.

Computed Tomography Window Blending: Feasibility in Thoracic Trauma.

PubMed

Mandell, Jacob C; Wortman, Jeremy R; Rocha, Tatiana C; Folio, Les R; Andriole, Katherine P; Khurana, Bharti

2018-02-07

This study aims to demonstrate the feasibility of processing computed tomography (CT) images with a custom window blending algorithm that combines soft-tissue, bone, and lung window settings into a single image; to compare the time for interpretation of chest CT for thoracic trauma with window blending and conventional window settings; and to assess diagnostic performance of both techniques. Adobe Photoshop was scripted to process axial DICOM images from retrospective contrast-enhanced chest CTs performed for trauma with a window-blending algorithm. Two emergency radiologists independently interpreted the axial images from 103 chest CTs with both blended and conventional windows. Interpretation time and diagnostic performance were compared with Wilcoxon signed-rank test and McNemar test, respectively. Agreement with Nexus CT Chest injury severity was assessed with the weighted kappa statistic. A total of 13,295 images were processed without error. Interpretation was faster with window blending, resulting in a 20.3% time saving (P < .001), with no difference in diagnostic performance, within the power of the study to detect a difference in sensitivity of 5% as determined by post hoc power analysis. The sensitivity of the window-blended cases was 82.7%, compared to 81.6% for conventional windows. The specificity of the window-blended cases was 93.1%, compared to 90.5% for conventional windows. All injuries of major clinical significance (per Nexus CT Chest criteria) were correctly identified in all reading sessions, and all negative cases were correctly classified. All readers demonstrated near-perfect agreement with injury severity classification with both window settings. In this pilot study utilizing retrospective data, window blending allows faster preliminary interpretation of axial chest CT performed for trauma, with no significant difference in diagnostic performance compared to conventional window settings. Future studies would be required to assess the utility of window blending in clinical practice. Copyright © 2018 The Association of University Radiologists. All rights reserved.

The Accuracy of Diagnostic Tests for Lyme Disease in Humans, A Systematic Review and Meta-Analysis of North American Research

PubMed Central

Lindsay, Robbin; Ogden, Nicholas

2016-01-01

There has been an increasing incidence of Lyme disease (LD) in Canada and the United States corresponding to the expanding range of the Ixodes tick vector and Lyme disease agent (Borrelia burgdorferi sensu stricto). There are many diagnostic tests for LD available in North America, all of which have some performance issues, and physicians are concerned about the appropriate use and interpretation of these tests. The objective of this systematic review is to summarize the North American evidence on the accuracy of diagnostic tests and test regimes at various stages of LD. Included in the review are 48 studies on diagnostic tests used in North America published since 1995. Thirteen studies examined a two-tier serological test protocol vs. clinical diagnosis, 24 studies examined single assays vs. clinical diagnosis, 9 studies examined single immunoblot vs. clinical diagnosis, 7 studies compared culture or PCR direct detection methods vs. clinical diagnosis, 22 studies compared two or more tests with each other and 8 studies compared a two-tiered serological test protocol to another test. Recent studies examining the sensitivity and specificity of various test protocols noted that the Immunetics® C6 B. burgdorferi ELISA™ and the two tier approach have superior specificity compared to proposed replacements, and the CDC recommended western blot algorithm has equivalent or superior specificity over other proposed test algorithms. There is a dramatic increase in test sensitivity with progression of B. burgdorferi infection from early to late LD. Direct detection methods, culture and PCR of tissue or blood samples were not as sensitive or timely compared to serological testing. It was also noted that there are a large number of both commercial (n = 42) and in-house developed tests used by private laboratories which have not been evaluated in the primary literature. PMID:28002488

Combining High Sensitivity Cardiac Troponin I and Cardiac Troponin T in the Early Diagnosis of Acute Myocardial Infarction.

PubMed

van der Linden, Noreen; Wildi, Karin; Twerenbold, Raphael; Pickering, John W; Than, Martin; Cullen, Louise; Greenslade, Jaimi; Parsonage, William; Nestelberger, Thomas; Boeddinghaus, Jasper; Badertscher, Patrick; Rubini Giménez, Maria; Klinkenberg, Lieke J J; Bekers, Otto; Schöni, Aline; Keller, Dagmar I; Sabti, Zaid; Puelacher, Christian; Cupa, Janosch; Schumacher, Lukas; Kozhuharov, Nikola; Grimm, Karin; Shrestha, Samyut; Flores, Dayana; Freese, Michael; Stelzig, Claudia; Strebel, Ivo; Miró, Òscar; Rentsch, Katharina; Morawiec, Beata; Kawecki, Damian; Kloos, Wanda; Lohrmann, Jens; Richards, A Mark; Troughton, Richard; Pemberton, Christopher; Osswald, Stefan; van Dieijen-Visser, Marja P; Mingels, Alma M; Reichlin, Tobias; Meex, Steven J R; Mueller, Christian

2018-04-24

Background -Combining two signals of cardiomyocyte injury, cardiac troponin I (cTnI) and T (cTnT), might overcome some individual pathophysiological and analytical limitations and thereby increase diagnostic accuracy for acute myocardial infarction (AMI) with a single blood draw. We aimed to evaluate the diagnostic performance of combinations of high sensitivity (hs) cTnI and hs-cTnT for the early diagnosis of AMI. Methods -The diagnostic performance of combining hs-cTnI (Architect, Abbott) and hs-cTnT (Elecsys, Roche) concentrations (sum, product, ratio and a combination algorithm) obtained at the time of presentation was evaluated in a large multicenter diagnostic study of patients with suspected AMI. The optimal rule out and rule in thresholds were externally validated in a second large multicenter diagnostic study. The proportion of patients eligible for early rule out was compared with the ESC 0/1 and 0/3 hour algorithms. Results -Combining hs-cTnI and hs-cTnT concentrations did not consistently increase overall diagnostic accuracy as compared with the individual isoforms. However, the combination improved the proportion of patients meeting criteria for very early rule-out. With the ESC 2015 guideline recommended algorithms and cut-offs, the proportion meeting rule out criteria after the baseline blood sampling was limited (6-24%) and assay dependent. Application of optimized cut-off values using the sum (9 ng/L) and product (18 ng2/L2) of hs-cTnI and hs-cTnT concentrations led to an increase in the proportion ruled-out after a single blood draw to 34-41% in the original (sum: negative predictive value (NPV) 100% (95%CI: 99.5-100%); product: NPV 100% (95%CI: 99.5-100%) and in the validation cohort (sum: NPV 99.6% (95%CI: 99.0-99.9%); product: NPV 99.4% (95%CI: 98.8-99.8%). The use of a combination algorithm (hs-cTnI <4 ng/L and hs-cTnT <9 ng/L) showed comparable results for rule out (40-43% ruled out; NPV original cohort 99.9% (95%CI: 99.2-100%); NPV validation cohort 99.5% (95%CI: 98.9-99.8%)) and rule-in (PPV original cohort 74.4% (95%Cl 69.6-78.8%); PPV validation cohort 84.0% (95%Cl 79.7-87.6%)). Conclusions -New strategies combining hs-cTnI and hs-cTnT concentrations may significantly increase the number of patients eligible for very early and safe rule-out, but do not seem helpful for the rule-in of AMI. Clinical Trial Registration -APACE URL: www.clinicaltrial.gov, Unique Identifier: NCT00470587; ADAPT URL: www.anzctr.org.au, Unique Identifier: ACTRN12611001069943.

New system for digital to analog transformation and reconstruction of 12-lead ECGs.

PubMed

Kothadia, Roshni; Kulecz, Walter B; Kofman, Igor S; Black, Adam J; Grier, James W; Schlegel, Todd T

2013-01-01

We describe initial validation of a new system for digital to analog conversion (DAC) and reconstruction of 12-lead ECGs. The system utilizes an open and optimized software format with a commensurately optimized DAC hardware configuration to accurately reproduce, from digital files, the original analog electrocardiographic signals of previously instrumented patients. By doing so, the system also ultimately allows for transmission of data collected on one manufacturer's 12-lead ECG hardware/software into that of any other. To initially validate the system, we compared original and post-DAC re-digitized 12-lead ECG data files (∼5-minutes long) in two types of validation studies in 10 patients. The first type quantitatively compared the total waveform voltage differences between the original and re-digitized data while the second type qualitatively compared the automated electrocardiographic diagnostic statements generated by the original versus re-digitized data. The grand-averaged difference in root mean squared voltage between the original and re-digitized data was 20.8 µV per channel when re-digitization involved the same manufacturer's analog to digital converter (ADC) as the original digitization, and 28.4 µV per channel when it involved a different manufacturer's ADC. Automated diagnostic statements generated by the original versus reconstructed data did not differ when using the diagnostic algorithm from the same manufacturer on whose device the original data were collected, and differed only slightly for just 1 of 10 patients when using a third-party diagnostic algorithm throughout. Original analog 12-lead ECG signals can be reconstructed from digital data files with accuracy sufficient for clinical use. Such reconstructions can readily enable automated second opinions for difficult-to-interpret 12-lead ECGs, either locally or remotely through the use of dedicated or cloud-based servers.

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.

PubMed

Haenssle, H A; Fink, C; Schneiderbauer, R; Toberer, F; Buhl, T; Blum, A; Kalloo, A; Hassen, A Ben Hadj; Thomas, L; Enk, A; Uhlmann, L

2018-05-28

Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking. Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification. This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).

Diagnostic accuracy of the Patient Health Questionnaire-9 for assessment of depression in type II diabetes mellitus and/or coronary heart disease in primary care.

PubMed

van der Zwaan, G Lennart; van Dijk, Susan E M; Adriaanse, Marcel C; van Marwijk, Harm W J; van Tulder, Maurits W; Pols, Alide D; Bosmans, Judith E

2016-01-15

Depression is common among type 2 diabetes mellitus (DM2)/coronary heart disease (CHD) patients and is associated with adverse health effects. A promising strategy to reduce burden of disease is to identify patients at risk for depression in order to offer indicated prevention. This study aims to assess the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9) to be used as a tool to identify high risk patients. In this cross-sectional study, 586 consecutive DM2/CHD patients aged >18 were recruited through 23 general practices. PHQ-9 outcomes were compared to the Mini International Neuropsychiatric Interview (MINI), which was considered the reference standard. Diagnostic accuracy was evaluated for minor and major depression, comparing both sum- and algorithm based PHQ-9 scores. For minor depression, the optimal cut-off score was 8 (sensitivity 71%, specificity 71% and an AUC of 0.74). For major depression, the optimal cut-off score was 10 resulting in a sensitivity of 84%, a specificity of 82%, and an AUC of 0.88. The positive predictive value of the PHQ-9 algorithm for diagnosing minor and major depression was 25% and 33%, respectively. Two main limitations apply. MINI Interviewers were not blinded for PHQ-9 scores and less than 10% of all invited patients could be included in the analyses. This could have resulted in biased outcomes. The PHQ-9 sum score performs well in identifying patients at high risk of minor and major depression. However, the PHQ-9 showed suboptimal results for diagnostic purposes. Therefore, it is recommended to combine the use of the PHQ-9 with further diagnostics to identify depression. Copyright © 2015 Elsevier B.V. All rights reserved.

Accuracy of Referring Provider and Endoscopist Impressions of Colonoscopy Indication.

PubMed

Naveed, Mariam; Clary, Meredith; Ahn, Chul; Kubiliun, Nisa; Agrawal, Deepak; Cryer, Byron; Murphy, Caitlin; Singal, Amit G

2017-07-01

Background: Referring provider and endoscopist impressions of colonoscopy indication are used for clinical care, reimbursement, and quality reporting decisions; however, the accuracy of these impressions is unknown. This study assessed the sensitivity, specificity, positive and negative predictive value, and overall accuracy of methods to classify colonoscopy indication, including referring provider impression, endoscopist impression, and administrative algorithm compared with gold standard chart review. Methods: We randomly sampled 400 patients undergoing a colonoscopy at a Veterans Affairs health system between January 2010 and December 2010. Referring provider and endoscopist impressions of colonoscopy indication were compared with gold-standard chart review. Indications were classified into 4 mutually exclusive categories: diagnostic, surveillance, high-risk screening, or average-risk screening. Results: Of 400 colonoscopies, 26% were performed for average-risk screening, 7% for high-risk screening, 26% for surveillance, and 41% for diagnostic indications. Accuracy of referring provider and endoscopist impressions of colonoscopy indication were 87% and 84%, respectively, which were significantly higher than that of the administrative algorithm (45%; P <.001 for both). There was substantial agreement between endoscopist and referring provider impressions (κ=0.76). All 3 methods showed high sensitivity (>90%) for determining screening (vs nonscreening) indication, but specificity of the administrative algorithm was lower (40.3%) compared with referring provider (93.7%) and endoscopist (84.0%) impressions. Accuracy of endoscopist, but not referring provider, impression was lower in patients with a family history of colon cancer than in those without (65% vs 84%; P =.001). Conclusions: Referring provider and endoscopist impressions of colonoscopy indication are both accurate and may be useful data to incorporate into algorithms classifying colonoscopy indication. Copyright © 2017 by the National Comprehensive Cancer Network.

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

NASA Technical Reports Server (NTRS)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.

Clinical study of quantitative diagnosis of early cervical cancer based on the classification of acetowhitening kinetics

NASA Astrophysics Data System (ADS)

Wu, Tao; Cheung, Tak-Hong; Yim, So-Fan; Qu, Jianan Y.

2010-03-01

A quantitative colposcopic imaging system for the diagnosis of early cervical cancer is evaluated in a clinical study. This imaging technology based on 3-D active stereo vision and motion tracking extracts diagnostic information from the kinetics of acetowhitening process measured from the cervix of human subjects in vivo. Acetowhitening kinetics measured from 137 cervical sites of 57 subjects are analyzed and classified using multivariate statistical algorithms. Cross-validation methods are used to evaluate the performance of the diagnostic algorithms. The results show that an algorithm for screening precancer produced 95% sensitivity (SE) and 96% specificity (SP) for discriminating normal and human papillomavirus (HPV)-infected tissues from cervical intraepithelial neoplasia (CIN) lesions. For a diagnostic algorithm, 91% SE and 90% SP are achieved for discriminating normal tissue, HPV infected tissue, and low-grade CIN lesions from high-grade CIN lesions. The results demonstrate that the quantitative colposcopic imaging system could provide objective screening and diagnostic information for early detection of cervical cancer.

Use of soft x-ray diagnostic on the COMPASS tokamak for investigations of sawteeth crash neighborhood and of plasma position using fast inversion methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Imrisek, M.; Faculty of Mathematics and Physics, Charles University in Prague, Prague; Weinzettl, V.

2014-11-15

The soft x-ray diagnostic is suitable for monitoring plasma activity in the tokamak core, e.g., sawtooth instability. Moreover, spatially resolved measurements can provide information about plasma position and shape, which can supplement magnetic measurements. In this contribution, fast algorithms with the potential for a real-time use are tested on the data from the COMPASS tokamak. In addition, the soft x-ray data are compared with data from other diagnostics in order to discuss possible connection between sawtooth instability on one side and the transition to higher confinement mode, edge localized modes and productions of runaway electrons on the other side.

Development of Multi-perspective Diagnostics and Analysis Algorithms with Applications to Subsonic and Supersonic Combustors

NASA Astrophysics Data System (ADS)

Wickersham, Andrew Joseph

There are two critical research needs for the study of hydrocarbon combustion in high speed flows: 1) combustion diagnostics with adequate temporal and spatial resolution, and 2) mathematical techniques that can extract key information from large datasets. The goal of this work is to address these needs, respectively, by the use of high speed and multi-perspective chemiluminescence and advanced mathematical algorithms. To obtain the measurements, this work explored the application of high speed chemiluminescence diagnostics and the use of fiber-based endoscopes (FBEs) for non-intrusive and multi-perspective chemiluminescence imaging up to 20 kHz. Non-intrusive and full-field imaging measurements provide a wealth of information for model validation and design optimization of propulsion systems. However, it is challenging to obtain such measurements due to various implementation difficulties such as optical access, thermal management, and equipment cost. This work therefore explores the application of FBEs for non-intrusive imaging to supersonic propulsion systems. The FBEs used in this work are demonstrated to overcome many of the aforementioned difficulties and provided datasets from multiple angular positions up to 20 kHz in a supersonic combustor. The combustor operated on ethylene fuel at Mach 2 with an inlet stagnation temperature and pressure of approximately 640 degrees Fahrenheit and 70 psia, respectively. The imaging measurements were obtained from eight perspectives simultaneously, providing full-field datasets under such flow conditions for the first time, allowing the possibility of inferring multi-dimensional measurements. Due to the high speed and multi-perspective nature, such new diagnostic capability generates a large volume of data and calls for analysis algorithms that can process the data and extract key physics effectively. To extract the key combustion dynamics from the measurements, three mathematical methods were investigated in this work: Fourier analysis, proper orthogonal decomposition (POD), and wavelet analysis (WA). These algorithms were first demonstrated and tested on imaging measurements obtained from one perspective in a sub-sonic combustor (up to Mach 0.2). The results show that these algorithms are effective in extracting the key physics from large datasets, including the characteristic frequencies of flow-flame interactions especially during transient processes such as lean blow off and ignition. After these relatively simple tests and demonstrations, these algorithms were applied to process the measurements obtained from multi-perspective in the supersonic combustor. compared to past analyses (which have been limited to data obtained from one perspective only), the availability of data at multiple perspective provide further insights into the flame and flow structures in high speed flows. In summary, this work shows that high speed chemiluminescence is a simple yet powerful combustion diagnostic. Especially when combined with FBEs and the analyses algorithms described in this work, such diagnostics provide full-field imaging at high repetition rate in challenging flows. Based on such measurements, a wealth of information can be obtained from proper analysis algorithms, including characteristic frequency, dominating flame modes, and even multi-dimensional flame and flow structures.

Incremental Yield of Including Determine-TB LAM Assay in Diagnostic Algorithms for Hospitalized and Ambulatory HIV-Positive Patients in Kenya.

PubMed

Huerga, Helena; Ferlazzo, Gabriella; Bevilacqua, Paolo; Kirubi, Beatrice; Ardizzoni, Elisa; Wanjala, Stephen; Sitienei, Joseph; Bonnet, Maryline

2017-01-01

Determine-TB LAM assay is a urine point-of-care test useful for TB diagnosis in HIV-positive patients. We assessed the incremental diagnostic yield of adding LAM to algorithms based on clinical signs, sputum smear-microscopy, chest X-ray and Xpert MTB/RIF in HIV-positive patients with symptoms of pulmonary TB (PTB). Prospective observational cohort of ambulatory (either severely ill or CD4<200cells/μl or with Body Mass Index<17Kg/m2) and hospitalized symptomatic HIV-positive adults in Kenya. Incremental diagnostic yield of adding LAM was the difference in the proportion of confirmed TB patients (positive Xpert or MTB culture) diagnosed by the algorithm with LAM compared to the algorithm without LAM. The multivariable mortality model was adjusted for age, sex, clinical severity, BMI, CD4, ART initiation, LAM result and TB confirmation. Among 474 patients included, 44.1% were severely ill, 69.6% had CD4<200cells/μl, 59.9% had initiated ART, 23.2% could not produce sputum. LAM, smear-microscopy, Xpert and culture in sputum were positive in 39.0% (185/474), 21.6% (76/352), 29.1% (102/350) and 39.7% (92/232) of the patients tested, respectively. Of 156 patients with confirmed TB, 65.4% were LAM positive. Of those classified as non-TB, 84.0% were LAM negative. Adding LAM increased the diagnostic yield of the algorithms by 36.6%, from 47.4% (95%CI:39.4-55.6) to 84.0% (95%CI:77.3-89.4%), when using clinical signs and X-ray; by 19.9%, from 62.2% (95%CI:54.1-69.8) to 82.1% (95%CI:75.1-87.7), when using clinical signs and microscopy; and by 13.4%, from 74.4% (95%CI:66.8-81.0) to 87.8% (95%CI:81.6-92.5), when using clinical signs and Xpert. LAM positive patients had an increased risk of 2-months mortality (aOR:2.7; 95%CI:1.5-4.9). LAM should be included in TB diagnostic algorithms in parallel to microscopy or Xpert request for HIV-positive patients either ambulatory (severely ill or CD4<200cells/μl) or hospitalized. LAM allows same day treatment initiation in patients at higher risk of death and in those not able to produce sputum.

Evaluation of the Effect of Diagnostic Molecular Testing on the Surgical Decision-Making Process for Patients With Thyroid Nodules.

PubMed

Noureldine, Salem I; Najafian, Alireza; Aragon Han, Patricia; Olson, Matthew T; Genther, Dane J; Schneider, Eric B; Prescott, Jason D; Agrawal, Nishant; Mathur, Aarti; Zeiger, Martha A; Tufano, Ralph P

2016-07-01

Diagnostic molecular testing is used in the workup of thyroid nodules. While these tests appear to be promising in more definitively assigning a risk of malignancy, their effect on surgical decision making has yet to be demonstrated. To investigate the effect of diagnostic molecular profiling of thyroid nodules on the surgical decision-making process. A surgical management algorithm was developed and published after peer review that incorporated individual Bethesda System for Reporting Thyroid Cytopathology classifications with clinical, laboratory, and radiological results. This algorithm was created to formalize the decision-making process selected herein in managing patients with thyroid nodules. Between April 1, 2014, and March 31, 2015, a prospective study of patients who had undergone diagnostic molecular testing of a thyroid nodule before being seen for surgical consultation was performed. The recommended management undertaken by the surgeon was then prospectively compared with the corresponding one in the algorithm. Patients with thyroid nodules who did not undergo molecular testing and were seen for surgical consultation during the same period served as a control group. All pertinent treatment options were presented to each patient, and any deviation from the algorithm was recorded prospectively. To evaluate the appropriateness of any change (deviation) in management, the surgical histopathology diagnosis was correlated with the surgery performed. The study cohort comprised 140 patients who underwent molecular testing. Their mean (SD) age was 50.3 (14.6) years, and 75.0% (105 of 140) were female. Over a 1-year period, 20.3% (140 of 688) had undergone diagnostic molecular testing before surgical consultation, and 79.7% (548 of 688) had not undergone molecular testing. The surgical management deviated from the treatment algorithm in 12.9% (18 of 140) with molecular testing and in 10.2% (56 of 548) without molecular testing (P = .37). In the group with molecular testing, the surgical management plan of only 7.9% (11 of 140) was altered as a result of the molecular test. All but 1 of those patients were found to be overtreated relative to the surgical histopathology analysis. Molecular testing did not significantly affect the surgical decision-making process in this study. Among patients whose treatment was altered based on these markers, there was evidence of overtreatment.

Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture.

PubMed

Esses, Steven J; Lu, Xiaoguang; Zhao, Tiejun; Shanbhogue, Krishna; Dane, Bari; Bruno, Mary; Chandarana, Hersh

2018-03-01

To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T 2 -weighted (T 2 WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists. We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351 T 2 WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171 T 2 WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171 T 2 WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists. There was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2). We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T 2 WI of the liver. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723-728. © 2017 International Society for Magnetic Resonance in Medicine.

FPGA based charge acquisition algorithm for soft x-ray diagnostics system

NASA Astrophysics Data System (ADS)

Wojenski, A.; Kasprowicz, G.; Pozniak, K. T.; Zabolotny, W.; Byszuk, A.; Juszczyk, B.; Kolasinski, P.; Krawczyk, R. D.; Zienkiewicz, P.; Chernyshova, M.; Czarski, T.

2015-09-01

Soft X-ray (SXR) measurement systems working in tokamaks or with laser generated plasma can expect high photon fluxes. Therefore it is necessary to focus on data processing algorithms to have the best possible efficiency in term of processed photon events per second. This paper refers to recently designed algorithm and data-flow for implementation of charge data acquisition in FPGA. The algorithms are currently on implementation stage for the soft X-ray diagnostics system. In this paper despite of the charge processing algorithm is also described general firmware overview, data storage methods and other key components of the measurement system. The simulation section presents algorithm performance and expected maximum photon rate.

Region of interest processing for iterative reconstruction in x-ray computed tomography

NASA Astrophysics Data System (ADS)

Kopp, Felix K.; Nasirudin, Radin A.; Mei, Kai; Fehringer, Andreas; Pfeiffer, Franz; Rummeny, Ernst J.; Noël, Peter B.

2015-03-01

The recent advancements in the graphics card technology raised the performance of parallel computing and contributed to the introduction of iterative reconstruction methods for x-ray computed tomography in clinical CT scanners. Iterative maximum likelihood (ML) based reconstruction methods are known to reduce image noise and to improve the diagnostic quality of low-dose CT. However, iterative reconstruction of a region of interest (ROI), especially ML based, is challenging. But for some clinical procedures, like cardiac CT, only a ROI is needed for diagnostics. A high-resolution reconstruction of the full field of view (FOV) consumes unnecessary computation effort that results in a slower reconstruction than clinically acceptable. In this work, we present an extension and evaluation of an existing ROI processing algorithm. Especially improvements for the equalization between regions inside and outside of a ROI are proposed. The evaluation was done on data collected from a clinical CT scanner. The performance of the different algorithms is qualitatively and quantitatively assessed. Our solution to the ROI problem provides an increase in signal-to-noise ratio and leads to visually less noise in the final reconstruction. The reconstruction speed of our technique was observed to be comparable with other previous proposed techniques. The development of ROI processing algorithms in combination with iterative reconstruction will provide higher diagnostic quality in the near future.

[X-ray endoscopic semiotics and diagnostic algorithm of radiation studies of preneoplastic gastric mucosa changes].

PubMed

Akberov, R F; Gorshkov, A N

1997-01-01

The X-ray endoscopic semiotics of precancerous gastric mucosal changes (epithelial dysplasia, intestinal epithelial rearrangement) was examined by the results of 1574 gastric examination. A diagnostic algorithm was developed for radiation studies in the diagnosis of the above pathology.

2012 HIV Diagnostics Conference: the molecular diagnostics perspective.

PubMed

Branson, Bernard M; Pandori, Mark

2013-04-01

2012 HIV Diagnostic Conference Atlanta, GA, USA, 12-14 December 2012. This report highlights the presentations and discussions from the 2012 National HIV Diagnostic Conference held in Atlanta (GA, USA), on 12-14 December 2012. Reflecting changes in the evolving field of HIV diagnostics, the conference provided a forum for evaluating developments in molecular diagnostics and their role in HIV diagnosis. In 2010, the HIV Diagnostics Conference concluded with the proposal of a new diagnostic algorithm which included nucleic acid testing to resolve discordant screening and supplemental antibody test results. The 2012 meeting, picking up where the 2010 meeting left off, focused on scientific presentations that assessed this new algorithm and the role played by RNA testing and new developments in molecular diagnostics, including detection of total and integrated HIV-1 DNA, detection and quantification of HIV-2 RNA, and rapid formats for detection of HIV-1 RNA.

Chaotic particle swarm optimization with mutation for classification.

PubMed

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

Combination of culture, antigen and toxin detection, and cytotoxin neutralization assay for optimal Clostridium difficile diagnostic testing

PubMed Central

Alfa, Michelle J; Sepehri, Shadi

2013-01-01

BACKGROUND: There has been a growing interest in developing an appropriate laboratory diagnostic algorithm for Clostridium difficile, mainly as a result of increases in both the number and severity of cases of C difficile infection in the past decade. A C difficile diagnostic algorithm is necessary because diagnostic kits, mostly for the detection of toxins A and B or glutamate dehydrogenase (GDH) antigen, are not sufficient as stand-alone assays for optimal diagnosis of C difficile infection. In addition, conventional reference methods for C difficile detection (eg, toxigenic culture and cytotoxin neutralization [CTN] assays) are not routinely practiced in diagnostic laboratory settings. OBJECTIVE: To review the four-step algorithm used at Diagnostic Services of Manitoba sites for the laboratory diagnosis of toxigenic C difficile. RESULT: One year of retrospective C difficile data using the proposed algorithm was reported. Of 5695 stool samples tested, 9.1% (n=517) had toxigenic C difficile. Sixty per cent (310 of 517) of toxigenic C difficile stools were detected following the first two steps of the algorithm. CTN confirmation of GDH-positive, toxin A- and B-negative assays resulted in detection of an additional 37.7% (198 of 517) of toxigenic C difficile. Culture of the third specimen, from patients who had two previous negative specimens, detected an additional 2.32% (12 of 517) of toxigenic C difficile samples. DISCUSSION: Using GDH antigen as the screening and toxin A and B as confirmatory test for C difficile, 85% of specimens were reported negative or positive within 4 h. Without CTN confirmation for GDH antigen and toxin A and B discordant results, 37% (195 of 517) of toxigenic C difficile stools would have been missed. Following the algorithm, culture was needed for only 2.72% of all specimens submitted for C difficile testing. CONCLUSION: The overview of the data illustrated the significance of each stage of this four-step C difficile algorithm and emphasized the value of using CTN assay and culture as parts of an algorithm that ensures accurate diagnosis of toxigenic C difficile. PMID:24421808

Diagnostic potential of Raman spectroscopy in Barrett's esophagus

NASA Astrophysics Data System (ADS)

Wong Kee Song, Louis-Michel; Molckovsky, Andrea; Wang, Kenneth K.; Burgart, Lawrence J.; Dolenko, Brion; Somorjai, Rajmund L.; Wilson, Brian C.

2005-04-01

Patients with Barrett's esophagus (BE) undergo periodic endoscopic surveillance with random biopsies in an effort to detect dysplastic or early cancerous lesions. Surveillance may be enhanced by near-infrared Raman spectroscopy (NIRS), which has the potential to identify endoscopically-occult dysplastic lesions within the Barrett's segment and allow for targeted biopsies. The aim of this study was to assess the diagnostic performance of NIRS for identifying dysplastic lesions in BE in vivo. Raman spectra (Pexc=70 mW; t=5 s) were collected from Barrett's mucosa at endoscopy using a custom-built NIRS system (λexc=785 nm) equipped with a filtered fiber-optic probe. Each probed site was biopsied for matching histological diagnosis as assessed by an expert pathologist. Diagnostic algorithms were developed using genetic algorithm-based feature selection and linear discriminant analysis, and classification was performed on all spectra with a bootstrap-based cross-validation scheme. The analysis comprised 192 samples (112 non-dysplastic, 54 low-grade dysplasia and 26 high-grade dysplasia/early adenocarcinoma) from 65 patients. Compared with histology, NIRS differentiated dysplastic from non-dysplastic Barrett's samples with 86% sensitivity, 88% specificity and 87% accuracy. NIRS identified 'high-risk' lesions (high-grade dysplasia/early adenocarcinoma) with 88% sensitivity, 89% specificity and 89% accuracy. In the present study, NIRS classified Barrett's epithelia with high and clinically-useful diagnostic accuracy.

Autism Diagnostic Interview-Revised (ADI-R) Algorithms for Toddlers and Young Preschoolers: Application in a Non-US Sample of 1,104 Children

ERIC Educational Resources Information Center

de Bildt, Annelies; Sytema, Sjoerd; Zander, Eric; Bölte, Sven; Sturm, Harald; Yirmiya, Nurit; Yaari, Maya; Charman, Tony; Salomone, Erica; LeCouteur, Ann; Green, Jonathan; Bedia, Ricardo Canal; Primo, Patricia García; van Daalen, Emma; de Jonge, Maretha V.; Guðmundsdóttir, Emilía; Jóhannsdóttir, Sigurrós; Raleva, Marija; Boskovska, Meri; Rogé, Bernadette; Baduel, Sophie; Moilanen, Irma; Yliherva, Anneli; Buitelaar, Jan; Oosterling, Iris J.

2015-01-01

The current study aimed to investigate the Autism Diagnostic Interview-Revised (ADI-R) algorithms for toddlers and young preschoolers (Kim and Lord, "J Autism Dev Disord" 42(1):82-93, 2012) in a non-US sample from ten sites in nine countries (n = 1,104). The construct validity indicated a good fit of the algorithms. The diagnostic…

Recommendations for the inclusion of Fabry disease as a rare febrile condition in existing algorithms for fever of unknown origin.

PubMed

Manna, Raffaele; Cauda, Roberto; Feriozzi, Sandro; Gambaro, Giovanni; Gasbarrini, Antonio; Lacombe, Didier; Livneh, Avi; Martini, Alberto; Ozdogan, Huri; Pisani, Antonio; Riccio, Eleonora; Verrecchia, Elena; Dagna, Lorenzo

2017-10-01

Fever of unknown origin (FUO) is a rather rare clinical syndrome representing a major diagnostic challenge. The occurrence of more than three febrile attacks with fever-free intervals of variable duration during 6 months of observation has recently been proposed as a subcategory of FUO, Recurrent FUO (RFUO). A substantial number of patients with RFUO have auto-inflammatory genetic fevers, but many patients remain undiagnosed. We hypothesize that this undiagnosed subgroup may be comprised of, at least in part, a number of rare genetic febrile diseases such as Fabry disease. We aimed to identify key features or potential diagnostic clues for Fabry disease as a model of rare genetic febrile diseases causing RFUO, and to develop diagnostic guidelines for RFUO, using Fabry disease as an example of inserting other rare diseases in the existing FUO algorithms. An international panel of specialists in recurrent fevers and rare diseases, including internists, infectious disease specialists, rheumatologists, gastroenterologists, nephrologists, and medical geneticists convened to review the existing diagnostic algorithms, and to suggest recommendations for arriving at accurate diagnoses on the basis of available literature and clinical experience. By combining specific features of rare diseases with other diagnostic considerations, guidelines have been designed to raise awareness and identify rare diseases among other causes of FUO. The proposed guidelines may be useful for the inclusion of rare diseases in the diagnostic algorithms for FUO. A wide spectrum of patients will be needed to validate the algorithm in different clinical settings.

A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

PubMed Central

Zhu, Bohui; Ding, Yongsheng; Hao, Kuangrong

2013-01-01

This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. PMID:23690875

The diagnosis of urinary tract infections in young children (DUTY): protocol for a diagnostic and prospective observational study to derive and validate a clinical algorithm for the diagnosis of UTI in children presenting to primary care with an acute illness.

PubMed

Downing, Harriet; Thomas-Jones, Emma; Gal, Micaela; Waldron, Cherry-Ann; Sterne, Jonathan; Hollingworth, William; Hood, Kerenza; Delaney, Brendan; Little, Paul; Howe, Robin; Wootton, Mandy; Macgowan, Alastair; Butler, Christopher C; Hay, Alastair D

2012-07-19

Urinary tract infection (UTI) is common in children, and may cause serious illness and recurrent symptoms. However, obtaining a urine sample from young children in primary care is challenging and not feasible for large numbers. Evidence regarding the predictive value of symptoms, signs and urinalysis for UTI in young children is urgently needed to help primary care clinicians better identify children who should be investigated for UTI. This paper describes the protocol for the Diagnosis of Urinary Tract infection in Young children (DUTY) study. The overall study aim is to derive and validate a cost-effective clinical algorithm for the diagnosis of UTI in children presenting to primary care acutely unwell. DUTY is a multicentre, diagnostic and prospective observational study aiming to recruit at least 7,000 children aged before their fifth birthday, being assessed in primary care for any acute, non-traumatic, illness of ≤ 28 days duration. Urine samples will be obtained from eligible consented children, and data collected on medical history and presenting symptoms and signs. Urine samples will be dipstick tested in general practice and sent for microbiological analysis. All children with culture positive urines and a random sample of children with urine culture results in other, non-positive categories will be followed up to record symptom duration and healthcare resource use. A diagnostic algorithm will be constructed and validated and an economic evaluation conducted.The primary outcome will be a validated diagnostic algorithm using a reference standard of a pure/predominant growth of at least >103, but usually >105 CFU/mL of one, but no more than two uropathogens.We will use logistic regression to identify the clinical predictors (i.e. demographic, medical history, presenting signs and symptoms and urine dipstick analysis results) most strongly associated with a positive urine culture result. We will then use economic evaluation to compare the cost effectiveness of the candidate prediction rules. This study will provide novel, clinically important information on the diagnostic features of childhood UTI and the cost effectiveness of a validated prediction rule, to help primary care clinicians improve the efficiency of their diagnostic strategy for UTI in young children.

The diagnosis of urinary tract infections in young children (DUTY): protocol for a diagnostic and prospective observational study to derive and validate a clinical algorithm for the diagnosis of UTI in children presenting to primary care with an acute illness

PubMed Central

2012-01-01

Background Urinary tract infection (UTI) is common in children, and may cause serious illness and recurrent symptoms. However, obtaining a urine sample from young children in primary care is challenging and not feasible for large numbers. Evidence regarding the predictive value of symptoms, signs and urinalysis for UTI in young children is urgently needed to help primary care clinicians better identify children who should be investigated for UTI. This paper describes the protocol for the Diagnosis of Urinary Tract infection in Young children (DUTY) study. The overall study aim is to derive and validate a cost-effective clinical algorithm for the diagnosis of UTI in children presenting to primary care acutely unwell. Methods/design DUTY is a multicentre, diagnostic and prospective observational study aiming to recruit at least 7,000 children aged before their fifth birthday, being assessed in primary care for any acute, non-traumatic, illness of ≤ 28 days duration. Urine samples will be obtained from eligible consented children, and data collected on medical history and presenting symptoms and signs. Urine samples will be dipstick tested in general practice and sent for microbiological analysis. All children with culture positive urines and a random sample of children with urine culture results in other, non-positive categories will be followed up to record symptom duration and healthcare resource use. A diagnostic algorithm will be constructed and validated and an economic evaluation conducted. The primary outcome will be a validated diagnostic algorithm using a reference standard of a pure/predominant growth of at least >103, but usually >105 CFU/mL of one, but no more than two uropathogens. We will use logistic regression to identify the clinical predictors (i.e. demographic, medical history, presenting signs and symptoms and urine dipstick analysis results) most strongly associated with a positive urine culture result. We will then use economic evaluation to compare the cost effectiveness of the candidate prediction rules. Discussion This study will provide novel, clinically important information on the diagnostic features of childhood UTI and the cost effectiveness of a validated prediction rule, to help primary care clinicians improve the efficiency of their diagnostic strategy for UTI in young children. PMID:22812651

CSE database: extended annotations and new recommendations for ECG software testing.

PubMed

Smíšek, Radovan; Maršánová, Lucie; Němcová, Andrea; Vítek, Martin; Kozumplík, Jiří; Nováková, Marie

2017-08-01

Nowadays, cardiovascular diseases represent the most common cause of death in western countries. Among various examination techniques, electrocardiography (ECG) is still a highly valuable tool used for the diagnosis of many cardiovascular disorders. In order to diagnose a person based on ECG, cardiologists can use automatic diagnostic algorithms. Research in this area is still necessary. In order to compare various algorithms correctly, it is necessary to test them on standard annotated databases, such as the Common Standards for Quantitative Electrocardiography (CSE) database. According to Scopus, the CSE database is the second most cited standard database. There were two main objectives in this work. First, new diagnoses were added to the CSE database, which extended its original annotations. Second, new recommendations for diagnostic software quality estimation were established. The ECG recordings were diagnosed by five new cardiologists independently, and in total, 59 different diagnoses were found. Such a large number of diagnoses is unique, even in terms of standard databases. Based on the cardiologists' diagnoses, a four-round consensus (4R consensus) was established. Such a 4R consensus means a correct final diagnosis, which should ideally be the output of any tested classification software. The accuracy of the cardiologists' diagnoses compared with the 4R consensus was the basis for the establishment of accuracy recommendations. The accuracy was determined in terms of sensitivity = 79.20-86.81%, positive predictive value = 79.10-87.11%, and the Jaccard coefficient = 72.21-81.14%, respectively. Within these ranges, the accuracy of the software is comparable with the accuracy of cardiologists. The accuracy quantification of the correct classification is unique. Diagnostic software developers can objectively evaluate the success of their algorithm and promote its further development. The annotations and recommendations proposed in this work will allow for faster development and testing of classification software. As a result, this might facilitate cardiologists' work and lead to faster diagnoses and earlier treatment.

[Implementation of cytology images classification--the Bethesda 2001 System--in a group of screened women from Podlaskie region--effect evaluation].

PubMed

Zbroch, Tomasz; Knapp, Paweł Grzegorz; Knapp, Piotr Andrzej

2007-09-01

Increasing knowledge concerning carcinogenesis within cervical epithelium has forced us to make continues modifications of cytology classification of the cervical smears. Eventually, new descriptions of the submicroscopic cytomorphological abnormalities have enabled the implementation of Bethesda System which was meant to take place of the former Papanicolaou classification although temporarily both are sometimes used simultaneously. The aim of this study was to compare results of these two classification systems in the aspect of diagnostic accuracy verified by further tests of the diagnostic algorithm for the cervical lesion evaluation. The study was conducted in the group of women selected from general population, the criteria being the place of living and cervical cancer age risk group, in the consecutive periods of mass screening in Podlaski region. The performed diagnostic tests have been based on the commonly used algorithm, as well as identical laboratory and methodological conditions. Performed assessment revealed comparable diagnostic accuracy of both analyzing classifications, verified by histological examination, although with marked higher specificity for dysplastic lesions with decreased number of HSIL results and increased diagnosis of LSILs. Higher number of performed colposcopies and biopsies were an additional consequence of TBS classification. Results based on Bethesda System made it possible to find the sources and reasons of abnormalities with much greater precision, which enabled causing agent treatment. Two evaluated cytology classification systems, although not much different, depicted higher potential of TBS and better, more effective communication between cytology laboratory and gynecologist, making reasonable implementation of The Bethesda System in the daily cytology screening work.

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data.

PubMed

Pesesky, Mitchell W; Hussain, Tahir; Wallace, Meghan; Patel, Sanket; Andleeb, Saadia; Burnham, Carey-Ann D; Dantas, Gautam

2016-01-01

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and incomplete genome assembly confounded the rules-based algorithm, resulting in predictions based on gene family, rather than on knowledge of the specific variant found. Low-frequency resistance caused errors in the machine-learning algorithm because those genes were not seen or seen infrequently in the test set. We also identified an example of variability in the phenotype-based results that led to disagreement with both genotype-based methods. Genotype-based antimicrobial susceptibility testing shows great promise as a diagnostic tool, and we outline specific research goals to further refine this methodology.

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil

2010-01-01

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

Meaningless comparisons lead to false optimism in medical machine learning

PubMed Central

Kording, Konrad; Recht, Benjamin

2017-01-01

A new trend in medicine is the use of algorithms to analyze big datasets, e.g. using everything your phone measures about you for diagnostics or monitoring. However, these algorithms are commonly compared against weak baselines, which may contribute to excessive optimism. To assess how well an algorithm works, scientists typically ask how well its output correlates with medically assigned scores. Here we perform a meta-analysis to quantify how the literature evaluates their algorithms for monitoring mental wellbeing. We find that the bulk of the literature (∼77%) uses meaningless comparisons that ignore patient baseline state. For example, having an algorithm that uses phone data to diagnose mood disorders would be useful. However, it is possible to explain over 80% of the variance of some mood measures in the population by simply guessing that each patient has their own average mood—the patient-specific baseline. Thus, an algorithm that just predicts that our mood is like it usually is can explain the majority of variance, but is, obviously, entirely useless. Comparing to the wrong (population) baseline has a massive effect on the perceived quality of algorithms and produces baseless optimism in the field. To solve this problem we propose “user lift” that reduces these systematic errors in the evaluation of personalized medical monitoring. PMID:28949964

Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study

PubMed Central

Gilman, Robert H; Tielsch, James M; Steinhoff, Mark; Figueroa, Dante; Rodriguez, Shalim; Caffo, Brian; Tracey, Brian; Elhilali, Mounya; West, James; Checkley, William

2012-01-01

Introduction WHO case management algorithm for paediatric pneumonia relies solely on symptoms of shortness of breath or cough and tachypnoea for treatment and has poor diagnostic specificity, tends to increase antibiotic resistance. Alternatives, including oxygen saturation measurement, chest ultrasound and chest auscultation, exist but with potential disadvantages. Electronic auscultation has potential for improved detection of paediatric pneumonia but has yet to be standardised. The authors aim to investigate the use of electronic auscultation to improve the specificity of the current WHO algorithm in developing countries. Methods This study is designed to test the hypothesis that pulmonary pathology can be differentiated from normal using computerised lung sound analysis (CLSA). The authors will record lung sounds from 600 children aged ≤5 years, 100 each with consolidative pneumonia, diffuse interstitial pneumonia, asthma, bronchiolitis, upper respiratory infections and normal lungs at a children's hospital in Lima, Peru. The authors will compare CLSA with the WHO algorithm and other detection approaches, including physical exam findings, chest ultrasound and microbiologic testing to construct an improved algorithm for pneumonia diagnosis. Discussion This study will develop standardised methods for electronic auscultation and chest ultrasound and compare their utility for detection of pneumonia to standard approaches. Utilising signal processing techniques, the authors aim to characterise lung sounds and through machine learning, develop a classification system to distinguish pathologic sounds. Data will allow a better understanding of the benefits and limitations of novel diagnostic techniques in paediatric pneumonia. PMID:22307098

A Framework to Debug Diagnostic Matrices

NASA Technical Reports Server (NTRS)

Kodal, Anuradha; Robinson, Peter; Patterson-Hine, Ann

2013-01-01

Diagnostics is an important concept in system health and monitoring of space operations. Many of the existing diagnostic algorithms utilize system knowledge in the form of diagnostic matrix (D-matrix, also popularly known as diagnostic dictionary, fault signature matrix or reachability matrix) gleaned from physical models. But, sometimes, this may not be coherent to obtain high diagnostic performance. In such a case, it is important to modify this D-matrix based on knowledge obtained from other sources such as time-series data stream (simulated or maintenance data) within the context of a framework that includes the diagnostic/inference algorithm. A systematic and sequential update procedure, diagnostic modeling evaluator (DME) is proposed to modify D-matrix and wrapper logic considering least expensive solution first. This iterative procedure includes conditions ranging from modifying 0s and 1s in the matrix, or adding/removing the rows (failure sources) columns (tests). We will experiment this framework on datasets from DX challenge 2009.

Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm.

PubMed

Chapman, Brian E; Lee, Sean; Kang, Hyunseok Peter; Chapman, Wendy W

2011-10-01

In this paper we describe an application called peFinder for document-level classification of CT pulmonary angiography reports. peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents. peFinder was used to answer questions about the disease state (pulmonary emboli present or absent), the certainty state of the diagnosis (uncertainty present or absent), the temporal state of an identified pulmonary embolus (acute or chronic), and the technical quality state of the exam (diagnostic or not diagnostic). Gold standard answers for each question were determined from the consensus classifications of three human annotators. peFinder results were compared to naive Bayes' classifiers using unigrams and bigrams. The sensitivities (and positive predictive values) for peFinder were 0.98(0.83), 0.86(0.96), 0.94(0.93), and 0.60(0.90) for disease state, quality state, certainty state, and temporal state respectively, compared to 0.68(0.77), 0.67(0.87), 0.62(0.82), and 0.04(0.25) for the naive Bayes' classifier using unigrams, and 0.75(0.79), 0.52(0.69), 0.59(0.84), and 0.04(0.25) for the naive Bayes' classifier using bigrams. Copyright © 2011 Elsevier Inc. All rights reserved.

Eosinophilic pustular folliculitis: A proposal of diagnostic and therapeutic algorithms.

PubMed

Nomura, Takashi; Katoh, Mayumi; Yamamoto, Yosuke; Miyachi, Yoshiki; Kabashima, Kenji

2016-11-01

Eosinophilic pustular folliculitis (EPF) is a sterile inflammatory dermatosis of unknown etiology. In addition to classic EPF, which affects otherwise healthy individuals, an immunocompromised state can cause immunosuppression-associated EPF (IS-EPF), which may be referred to dermatologists in inpatient services for assessments. Infancy-associated EPF (I-EPF) is the least characterized subtype, being observed mainly in non-Japanese infants. Diagnosis of EPF is challenging because its lesions mimic those of other common diseases, such as acne and dermatomycosis. Furthermore, there is no consensus regarding the treatment for each subtype of EPF. Here, we created procedure algorithms that facilitate the diagnosis and selection of therapeutic options on the basis of published work available in the public domain. Our diagnostic algorithm comprised a simple flowchart to direct physicians toward proper diagnosis. Recommended regimens were summarized in an easy-to-comprehend therapeutic algorithm for each subtype of EPF. These algorithms would facilitate the diagnostic and therapeutic procedure of EPF. © 2016 Japanese Dermatological Association.

Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination.

PubMed

Duraipandian, Shiyamala; Sylvest Bergholt, Mads; Zheng, Wei; Yu Ho, Khek; Teh, Ming; Guan Yeoh, Khay; Bok Yan So, Jimmy; Shabbir, Asim; Huang, Zhiwei

2012-08-01

Optical spectroscopic techniques including reflectance, fluorescence and Raman spectroscopy have shown promising potential for in vivo precancer and cancer diagnostics in a variety of organs. However, data-analysis has mostly been limited to post-processing and off-line algorithm development. In this work, we develop a fully automated on-line Raman spectral diagnostics framework integrated with a multimodal image-guided Raman technique for real-time in vivo cancer detection at endoscopy. A total of 2748 in vivo gastric tissue spectra (2465 normal and 283 cancer) were acquired from 305 patients recruited to construct a spectral database for diagnostic algorithms development. The novel diagnostic scheme developed implements on-line preprocessing, outlier detection based on principal component analysis statistics (i.e., Hotelling's T2 and Q-residuals) for tissue Raman spectra verification as well as for organ specific probabilistic diagnostics using different diagnostic algorithms. Free-running optical diagnosis and processing time of < 0.5 s can be achieved, which is critical to realizing real-time in vivo tissue diagnostics during clinical endoscopic examination. The optimized partial least squares-discriminant analysis (PLS-DA) models based on the randomly resampled training database (80% for learning and 20% for testing) provide the diagnostic accuracy of 85.6% [95% confidence interval (CI): 82.9% to 88.2%] [sensitivity of 80.5% (95% CI: 71.4% to 89.6%) and specificity of 86.2% (95% CI: 83.6% to 88.7%)] for the detection of gastric cancer. The PLS-DA algorithms are further applied prospectively on 10 gastric patients at gastroscopy, achieving the predictive accuracy of 80.0% (60/75) [sensitivity of 90.0% (27/30) and specificity of 73.3% (33/45)] for in vivo diagnosis of gastric cancer. The receiver operating characteristics curves further confirmed the efficacy of Raman endoscopy together with PLS-DA algorithms for in vivo prospective diagnosis of gastric cancer. This work successfully moves biomedical Raman spectroscopic technique into real-time, on-line clinical cancer diagnosis, especially in routine endoscopic diagnostic applications.

Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination

NASA Astrophysics Data System (ADS)

Duraipandian, Shiyamala; Sylvest Bergholt, Mads; Zheng, Wei; Yu Ho, Khek; Teh, Ming; Guan Yeoh, Khay; Bok Yan So, Jimmy; Shabbir, Asim; Huang, Zhiwei

2012-08-01

Optical spectroscopic techniques including reflectance, fluorescence and Raman spectroscopy have shown promising potential for in vivo precancer and cancer diagnostics in a variety of organs. However, data-analysis has mostly been limited to post-processing and off-line algorithm development. In this work, we develop a fully automated on-line Raman spectral diagnostics framework integrated with a multimodal image-guided Raman technique for real-time in vivo cancer detection at endoscopy. A total of 2748 in vivo gastric tissue spectra (2465 normal and 283 cancer) were acquired from 305 patients recruited to construct a spectral database for diagnostic algorithms development. The novel diagnostic scheme developed implements on-line preprocessing, outlier detection based on principal component analysis statistics (i.e., Hotelling's T2 and Q-residuals) for tissue Raman spectra verification as well as for organ specific probabilistic diagnostics using different diagnostic algorithms. Free-running optical diagnosis and processing time of < 0.5 s can be achieved, which is critical to realizing real-time in vivo tissue diagnostics during clinical endoscopic examination. The optimized partial least squares-discriminant analysis (PLS-DA) models based on the randomly resampled training database (80% for learning and 20% for testing) provide the diagnostic accuracy of 85.6% [95% confidence interval (CI): 82.9% to 88.2%] [sensitivity of 80.5% (95% CI: 71.4% to 89.6%) and specificity of 86.2% (95% CI: 83.6% to 88.7%)] for the detection of gastric cancer. The PLS-DA algorithms are further applied prospectively on 10 gastric patients at gastroscopy, achieving the predictive accuracy of 80.0% (60/75) [sensitivity of 90.0% (27/30) and specificity of 73.3% (33/45)] for in vivo diagnosis of gastric cancer. The receiver operating characteristics curves further confirmed the efficacy of Raman endoscopy together with PLS-DA algorithms for in vivo prospective diagnosis of gastric cancer. This work successfully moves biomedical Raman spectroscopic technique into real-time, on-line clinical cancer diagnosis, especially in routine endoscopic diagnostic applications.

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2002-01-01

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

Resonance Raman of BCC and normal skin

NASA Astrophysics Data System (ADS)

Liu, Cheng-hui; Sriramoju, Vidyasagar; Boydston-White, Susie; Wu, Binlin; Zhang, Chunyuan; Pei, Zhe; Sordillo, Laura; Beckman, Hugh; Alfano, Robert R.

2017-02-01

The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.

Evaluation of an iterative model-based CT reconstruction algorithm by intra-patient comparison of standard and ultra-low-dose examinations.

PubMed

Noël, Peter B; Engels, Stephan; Köhler, Thomas; Muenzel, Daniela; Franz, Daniela; Rasper, Michael; Rummeny, Ernst J; Dobritz, Martin; Fingerle, Alexander A

2018-01-01

Background The explosive growth of computer tomography (CT) has led to a growing public health concern about patient and population radiation dose. A recently introduced technique for dose reduction, which can be combined with tube-current modulation, over-beam reduction, and organ-specific dose reduction, is iterative reconstruction (IR). Purpose To evaluate the quality, at different radiation dose levels, of three reconstruction algorithms for diagnostics of patients with proven liver metastases under tumor follow-up. Material and Methods A total of 40 thorax-abdomen-pelvis CT examinations acquired from 20 patients in a tumor follow-up were included. All patients were imaged using the standard-dose and a specific low-dose CT protocol. Reconstructed slices were generated by using three different reconstruction algorithms: a classical filtered back projection (FBP); a first-generation iterative noise-reduction algorithm (iDose4); and a next generation model-based IR algorithm (IMR). Results The overall detection of liver lesions tended to be higher with the IMR algorithm than with FBP or iDose4. The IMR dataset at standard dose yielded the highest overall detectability, while the low-dose FBP dataset showed the lowest detectability. For the low-dose protocols, a significantly improved detectability of the liver lesion can be reported compared to FBP or iDose 4 ( P = 0.01). The radiation dose decreased by an approximate factor of 5 between the standard-dose and the low-dose protocol. Conclusion The latest generation of IR algorithms significantly improved the diagnostic image quality and provided virtually noise-free images for ultra-low-dose CT imaging.

Validation of an automated electronic algorithm and "dashboard" to identify and characterize decompensated heart failure admissions across a medical center.

PubMed

Cox, Zachary L; Lewis, Connie M; Lai, Pikki; Lenihan, Daniel J

2017-01-01

We aim to validate the diagnostic performance of the first fully automatic, electronic heart failure (HF) identification algorithm and evaluate the implementation of an HF Dashboard system with 2 components: real-time identification of decompensated HF admissions and accurate characterization of disease characteristics and medical therapy. We constructed an HF identification algorithm requiring 3 of 4 identifiers: B-type natriuretic peptide >400 pg/mL; admitting HF diagnosis; history of HF International Classification of Disease, Ninth Revision, diagnosis codes; and intravenous diuretic administration. We validated the diagnostic accuracy of the components individually (n = 366) and combined in the HF algorithm (n = 150) compared with a blinded provider panel in 2 separate cohorts. We built an HF Dashboard within the electronic medical record characterizing the disease and medical therapies of HF admissions identified by the HF algorithm. We evaluated the HF Dashboard's performance over 26 months of clinical use. Individually, the algorithm components displayed variable sensitivity and specificity, respectively: B-type natriuretic peptide >400 pg/mL (89% and 87%); diuretic (80% and 92%); and International Classification of Disease, Ninth Revision, code (56% and 95%). The HF algorithm achieved a high specificity (95%), positive predictive value (82%), and negative predictive value (85%) but achieved limited sensitivity (56%) secondary to missing provider-generated identification data. The HF Dashboard identified and characterized 3147 HF admissions over 26 months. Automated identification and characterization systems can be developed and used with a substantial degree of specificity for the diagnosis of decompensated HF, although sensitivity is limited by clinical data input. Copyright © 2016 Elsevier Inc. All rights reserved.

An Efficient Reachability Analysis Algorithm

NASA Technical Reports Server (NTRS)

Vatan, Farrokh; Fijany, Amir

2008-01-01

A document discusses a new algorithm for generating higher-order dependencies for diagnostic and sensor placement analysis when a system is described with a causal modeling framework. This innovation will be used in diagnostic and sensor optimization and analysis tools. Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in-situ platforms. This algorithm will serve as a power tool for technologies that satisfy a key requirement of autonomous spacecraft, including science instruments and in-situ missions.

Prosthetic joint infection development of an evidence-based diagnostic algorithm.

PubMed

Mühlhofer, Heinrich M L; Pohlig, Florian; Kanz, Karl-Georg; Lenze, Ulrich; Lenze, Florian; Toepfer, Andreas; Kelch, Sarah; Harrasser, Norbert; von Eisenhart-Rothe, Rüdiger; Schauwecker, Johannes

2017-03-09

Increasing rates of prosthetic joint infection (PJI) have presented challenges for general practitioners, orthopedic surgeons and the health care system in the recent years. The diagnosis of PJI is complex; multiple diagnostic tools are used in the attempt to correctly diagnose PJI. Evidence-based algorithms can help to identify PJI using standardized diagnostic steps. We reviewed relevant publications between 1990 and 2015 using a systematic literature search in MEDLINE and PUBMED. The selected search results were then classified into levels of evidence. The keywords were prosthetic joint infection, biofilm, diagnosis, sonication, antibiotic treatment, implant-associated infection, Staph. aureus, rifampicin, implant retention, pcr, maldi-tof, serology, synovial fluid, c-reactive protein level, total hip arthroplasty (THA), total knee arthroplasty (TKA) and combinations of these terms. From an initial 768 publications, 156 publications were stringently reviewed. Publications with class I-III recommendations (EAST) were considered. We developed an algorithm for the diagnostic approach to display the complex diagnosis of PJI in a clear and logically structured process according to ISO 5807. The evidence-based standardized algorithm combines modern clinical requirements and evidence-based treatment principles. The algorithm provides a detailed transparent standard operating procedure (SOP) for diagnosing PJI. Thus, consistently high, examiner-independent process quality is assured to meet the demands of modern quality management in PJI diagnosis.

Towards a Framework for Evaluating and Comparing Diagnosis Algorithms

NASA Technical Reports Server (NTRS)

Kurtoglu, Tolga; Narasimhan, Sriram; Poll, Scott; Garcia,David; Kuhn, Lukas; deKleer, Johan; vanGemund, Arjan; Feldman, Alexander

2009-01-01

Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics.

Mining Peripheral Arterial Disease Cases from Narrative Clinical Notes Using Natural Language Processing

PubMed Central

Afzal, Naveed; Sohn, Sunghwan; Abram, Sara; Scott, Christopher G.; Chaudhry, Rajeev; Liu, Hongfang; Kullo, Iftikhar J.; Arruda-Olson, Adelaide M.

2016-01-01

Objective Lower extremity peripheral arterial disease (PAD) is highly prevalent and affects millions of individuals worldwide. We developed a natural language processing (NLP) system for automated ascertainment of PAD cases from clinical narrative notes and compared the performance of the NLP algorithm to billing code algorithms, using ankle-brachial index (ABI) test results as the gold standard. Methods We compared the performance of the NLP algorithm to 1) results of gold standard ABI; 2) previously validated algorithms based on relevant ICD-9 diagnostic codes (simple model) and 3) a combination of ICD-9 codes with procedural codes (full model). A dataset of 1,569 PAD patients and controls was randomly divided into training (n= 935) and testing (n= 634) subsets. Results We iteratively refined the NLP algorithm in the training set including narrative note sections, note types and service types, to maximize its accuracy. In the testing dataset, when compared with both simple and full models, the NLP algorithm had better accuracy (NLP: 91.8%, full model: 81.8%, simple model: 83%, P<.001), PPV (NLP: 92.9%, full model: 74.3%, simple model: 79.9%, P<.001), and specificity (NLP: 92.5%, full model: 64.2%, simple model: 75.9%, P<.001). Conclusions A knowledge-driven NLP algorithm for automatic ascertainment of PAD cases from clinical notes had greater accuracy than billing code algorithms. Our findings highlight the potential of NLP tools for rapid and efficient ascertainment of PAD cases from electronic health records to facilitate clinical investigation and eventually improve care by clinical decision support. PMID:28189359

Steroidogenic Factor 1, Pit-1, and Adrenocorticotropic Hormone: A Rational Starting Place for the Immunohistochemical Characterization of Pituitary Adenoma.

PubMed

McDonald, William C; Banerji, Nilanjana; McDonald, Kelsey N; Ho, Bridget; Macias, Virgilia; Kajdacsy-Balla, Andre

2017-01-01

-Pituitary adenoma classification is complex, and diagnostic strategies vary greatly from laboratory to laboratory. No optimal diagnostic algorithm has been defined. -To develop a panel of immunohistochemical (IHC) stains that provides the optimal combination of cost, accuracy, and ease of use. -We examined 136 pituitary adenomas with stains of steroidogenic factor 1 (SF-1), Pit-1, anterior pituitary hormones, cytokeratin CAM5.2, and α subunit of human chorionic gonadotropin. Immunohistochemical staining was scored using the Allred system. Adenomas were assigned to a gold standard class based on IHC results and available clinical and serologic information. Correlation and cluster analyses were used to develop an algorithm for parsimoniously classifying adenomas. -The algorithm entailed a 1- or 2-step process: (1) a screening step consisting of IHC stains for SF-1, Pit-1, and adrenocorticotropic hormone; and (2) when screening IHC pattern and clinical history were not clearly gonadotrophic (SF-1 positive only), corticotrophic (adrenocorticotropic hormone positive only), or IHC null cell (negative-screening IHC), we subsequently used IHC for prolactin, growth hormone, thyroid-stimulating hormone, and cytokeratin CAM5.2. -Comparison between diagnoses generated by our algorithm and the gold standard diagnoses showed excellent agreement. When compared with a commonly used panel using 6 IHC for anterior pituitary hormones plus IHC for a low-molecular-weight cytokeratin in certain tumors, our algorithm uses approximately one-third fewer IHC stains and detects gonadotroph adenomas with greater sensitivity.

Non-negative Matrix Factorization and Co-clustering: A Promising Tool for Multi-tasks Bearing Fault Diagnosis

NASA Astrophysics Data System (ADS)

Shen, Fei; Chen, Chao; Yan, Ruqiang

2017-05-01

Classical bearing fault diagnosis methods, being designed according to one specific task, always pay attention to the effectiveness of extracted features and the final diagnostic performance. However, most of these approaches suffer from inefficiency when multiple tasks exist, especially in a real-time diagnostic scenario. A fault diagnosis method based on Non-negative Matrix Factorization (NMF) and Co-clustering strategy is proposed to overcome this limitation. Firstly, some high-dimensional matrixes are constructed using the Short-Time Fourier Transform (STFT) features, where the dimension of each matrix equals to the number of target tasks. Then, the NMF algorithm is carried out to obtain different components in each dimension direction through optimized matching, such as Euclidean distance and divergence distance. Finally, a Co-clustering technique based on information entropy is utilized to realize classification of each component. To verity the effectiveness of the proposed approach, a series of bearing data sets were analysed in this research. The tests indicated that although the diagnostic performance of single task is comparable to traditional clustering methods such as K-mean algorithm and Guassian Mixture Model, the accuracy and computational efficiency in multi-tasks fault diagnosis are improved.

Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label.

PubMed

Xu, Jin; Xu, Zhao-Xia; Lu, Ping; Guo, Rui; Yan, Hai-Xia; Xu, Wen-Jie; Wang, Yi-Qin; Xia, Chun-Ming

2016-11-01

To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint. Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL). REAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.

Electric machine differential for vehicle traction control and stability control

NASA Astrophysics Data System (ADS)

Kuruppu, Sandun Shivantha

Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.

Chaotic Particle Swarm Optimization with Mutation for Classification

PubMed Central

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion

PubMed Central

Bone, Daniel; Bishop, Somer; Black, Matthew P.; Goodwin, Matthew S.; Lord, Catherine; Narayanan, Shrikanth S.

2016-01-01

Background Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely-used ASD screening and diagnostic tools. Methods The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders (DD), split at age 10. Algorithms were created via a robust ML classifier, support vector machine (SVM), while targeting best-estimate clinical diagnosis of ASD vs. non-ASD. Parameter settings were tuned in multiple levels of cross-validation. Results The created algorithms were more effective (higher performing) than current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. Conclusions ML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight limitations of current caregiver-report instruments and indicate possible avenues for improving ASD screening and diagnostic tools. PMID:27090613

Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion.

PubMed

Bone, Daniel; Bishop, Somer L; Black, Matthew P; Goodwin, Matthew S; Lord, Catherine; Narayanan, Shrikanth S

2016-08-01

Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools. The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation. The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. ML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight the limitations of current caregiver-report instruments and indicate possible avenues for improving ASD screening and diagnostic tools. © 2016 Association for Child and Adolescent Mental Health.

Raman spectroscopy and PCA-SVM as a non-invasive diagnostic tool to identify and classify qualitatively glycated hemoglobin levels in vivo.

PubMed

Villa-Manríquez, J F; Castro-Ramos, J; Gutiérrez-Delgado, F; Lopéz-Pacheco, M A; Villanueva-Luna, A E

2017-08-01

In this study we identify and classify high and low levels of glycated hemoglobin (HbA1c) in healthy volunteers (HV) and diabetic patients (DP). Overall, 86 subjects were evaluated. The Raman spectrum was measured in three anatomical regions of the body: index fingertip, right ear lobe, and forehead. The measurements were performed to compare the difference between the HV and DP (22 well controlled diabetic patients (WCDP) (HbA1c <6.5%), and 49 not controlled diabetic patients (NCDP) (HbA1c ≥6.5%)). Multivariable methods such as principal components analysis (PCA) combined with support vector machine (SVM) were used to develop effective diagnostic algorithms for classification among these groups. The forehead of HV versus WCDP showed the highest sensitivity (100%) and specificity (100%). Sensitivity (100%) and specificity (60%), were highest in the forehead of WCDP, versus NCDP. In HV versus NCDP, the fingertip had the highest sensitivity (100%) and specificity (80%). The efficacy of the diagnostic algorithm by receiver operating characteristic (ROC) curve was confirmed. Overall, our study demonstrated that the combination of Raman spectroscopy and PCA-SVM are feasible non-invasive diagnostic tool in diabetes to classify qualitatively high and low levels of HbA1c in vivo. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cost-effectiveness of diagnostic-therapeutic strategies for paediatric visceral leishmaniasis in Morocco

PubMed Central

Alonso, Sergi; Tachfouti, Nabil; Najdi, Adil; Sicuri, Elisa; Picado, Albert

2017-01-01

Introduction Visceral leishmaniasis (VL) is a neglected parasitic disease with a high fatality rate if left untreated. Endemic in Morocco, as well as in other countries in the Mediterranean basin, VL mainly affects children living in rural areas. In Morocco, the direct observation of Leishmania parasites in bone marrow (BM) aspirates is used to diagnose VL and meglumine antimoniate (SB) is the first line of treatment. Less invasive, more efficacious and safer alternatives exist. In this study we estimate the cost-effectiveness of alternative diagnostic-therapeutic algorithms for paediatric VL in Morocco. Methods A decision tree was used to estimate the cost-effectiveness of using BM or rapid diagnostic tests (RDTs) as diagnostic tools and/or SB or two liposomal amphotericin B (L-AmB) regimens: 6-day and 2-day courses to treat VL. Incremental cost-effectiveness ratios, expressed as cost per death averted, were estimated by comparing costs and effectiveness of the alternative algorithms. A threshold analysis evaluated at which price L-AmB became cost-effective compared with current practices. Results Implementing RDT and/or L-AmB treatments would be cost-effective in Morocco according to the WHO thresholds. Introducing the 6-day course L-AmB, current second-line treatment, would be highly cost-effective if L-AmB price was below US$100/phial. The 2-day L-AmB treatment, current standard treatment of paediatric VL in France, is highly cost-effective, with L-AmB at its market price (US$165/phial). Conclusions The results of this study should encourage the implementation of RDT and/or short-course L-AmB treatments for paediatric VL management in Morocco and other North African countries. PMID:29018581

Cost-effectiveness of diagnostic-therapeutic strategies for paediatric visceral leishmaniasis in Morocco.

PubMed

Alonso, Sergi; Tachfouti, Nabil; Najdi, Adil; Sicuri, Elisa; Picado, Albert

2017-01-01

Visceral leishmaniasis (VL) is a neglected parasitic disease with a high fatality rate if left untreated. Endemic in Morocco, as well as in other countries in the Mediterranean basin, VL mainly affects children living in rural areas. In Morocco, the direct observation of Leishmania parasites in bone marrow (BM) aspirates is used to diagnose VL and meglumine antimoniate (SB) is the first line of treatment. Less invasive, more efficacious and safer alternatives exist. In this study we estimate the cost-effectiveness of alternative diagnostic-therapeutic algorithms for paediatric VL in Morocco. A decision tree was used to estimate the cost-effectiveness of using BM or rapid diagnostic tests (RDTs) as diagnostic tools and/or SB or two liposomal amphotericin B (L-AmB) regimens: 6-day and 2-day courses to treat VL. Incremental cost-effectiveness ratios, expressed as cost per death averted, were estimated by comparing costs and effectiveness of the alternative algorithms. A threshold analysis evaluated at which price L-AmB became cost-effective compared with current practices. Implementing RDT and/or L-AmB treatments would be cost-effective in Morocco according to the WHO thresholds. Introducing the 6-day course L-AmB, current second-line treatment, would be highly cost-effective if L-AmB price was below US$100/phial. The 2-day L-AmB treatment, current standard treatment of paediatric VL in France, is highly cost-effective, with L-AmB at its market price (US$165/phial). The results of this study should encourage the implementation of RDT and/or short-course L-AmB treatments for paediatric VL management in Morocco and other North African countries.

Prostate cancer prediction using the random forest algorithm that takes into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen.

PubMed

Xiao, Li-Hong; Chen, Pei-Ran; Gou, Zhong-Ping; Li, Yong-Zhong; Li, Mei; Xiang, Liang-Cheng; Feng, Ping

2017-01-01

The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels (P < 0.001), as well as in all transrectal ultrasound characteristics (P < 0.05) except uneven echo (P = 0.609). The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy.

New System for Digital to Analog Transformation and Reconstruction of 12-Lead ECGs

PubMed Central

Kothadia, Roshni; Kulecz, Walter B.; Kofman, Igor S.; Black, Adam J.; Grier, James W.; Schlegel, Todd T.

2013-01-01

Introduction We describe initial validation of a new system for digital to analog conversion (DAC) and reconstruction of 12-lead ECGs. The system utilizes an open and optimized software format with a commensurately optimized DAC hardware configuration to accurately reproduce, from digital files, the original analog electrocardiographic signals of previously instrumented patients. By doing so, the system also ultimately allows for transmission of data collected on one manufacturer's 12-lead ECG hardware/software into that of any other. Materials and Methods To initially validate the system, we compared original and post-DAC re-digitized 12-lead ECG data files (∼5-minutes long) in two types of validation studies in 10 patients. The first type quantitatively compared the total waveform voltage differences between the original and re-digitized data while the second type qualitatively compared the automated electrocardiographic diagnostic statements generated by the original versus re-digitized data. Results The grand-averaged difference in root mean squared voltage between the original and re-digitized data was 20.8 µV per channel when re-digitization involved the same manufacturer's analog to digital converter (ADC) as the original digitization, and 28.4 µV per channel when it involved a different manufacturer's ADC. Automated diagnostic statements generated by the original versus reconstructed data did not differ when using the diagnostic algorithm from the same manufacturer on whose device the original data were collected, and differed only slightly for just 1 of 10 patients when using a third-party diagnostic algorithm throughout. Conclusion Original analog 12-lead ECG signals can be reconstructed from digital data files with accuracy sufficient for clinical use. Such reconstructions can readily enable automated second opinions for difficult-to-interpret 12-lead ECGs, either locally or remotely through the use of dedicated or cloud-based servers. PMID:23613787

The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis

PubMed Central

Cassani, Raymundo; Falk, Tiago H.; Fraga, Francisco J.; Kanda, Paulo A. M.; Anghinah, Renato

2014-01-01

Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment. PMID:24723886

Feasibility of the TBDx automated digital microscopy system for the diagnosis of pulmonary tuberculosis.

PubMed

Nabeta, Pamela; Havumaki, Joshua; Ha, Dang Thi Minh; Caceres, Tatiana; Hang, Pham Thu; Collantes, Jimena; Thi Ngoc Lan, Nguyen; Gotuzzo, Eduardo; Denkinger, Claudia M

2017-01-01

Improved and affordable diagnostic or triage tests are urgently needed at the microscopy centre level. Automated digital microscopy has the potential to overcome issues related to conventional microscopy, including training time requirement and inconsistencies in results interpretation. For this blinded prospective study, sputum samples were collected from adults with presumptive pulmonary tuberculosis in Lima, Peru and Ho Chi Minh City, Vietnam. TBDx performance was evaluated as a stand-alone and as a triage test against conventional microscopy and Xpert, with culture as the reference standard. Xpert was used to confirm positive cases. A total of 613 subjects were enrolled between October 2014 and March 2015, with 539 included in the final analysis. The sensitivity of TBDx was 62·2% (95% CI 56·6-67·4) and specificity was 90·7% (95% CI 85·9-94·2) compared to culture. The algorithm assessing TBDx as a triage test achieved a specificity of 100% while maintaining sensitivity. While the diagnostic performance of TBDx did not reach the levels obtained by experienced microscopists in reference laboratories, it is conceivable that it would exceed the performance of less experienced microscopists. In the absence of highly sensitive and specific molecular tests at the microscopy centre level, TBDx in a triage-testing algorithm would optimize specificity and limit overall cost without compromising the number of patients receiving up-front drug susceptibility testing for rifampicin. However, the algorithm would miss over one third of patients compared to Xpert alone.

Validity and Reliability of Dermoscopic Criteria Used to Differentiate Nevi From Melanoma: A Web-Based International Dermoscopy Society Study.

PubMed

Carrera, Cristina; Marchetti, Michael A; Dusza, Stephen W; Argenziano, Giuseppe; Braun, Ralph P; Halpern, Allan C; Jaimes, Natalia; Kittler, Harald J; Malvehy, Josep; Menzies, Scott W; Pellacani, Giovanni; Puig, Susana; Rabinovitz, Harold S; Scope, Alon; Soyer, H Peter; Stolz, Wilhelm; Hofmann-Wellenhof, Rainer; Zalaudek, Iris; Marghoob, Ashfaq A

2016-07-01

The comparative diagnostic performance of dermoscopic algorithms and their individual criteria are not well studied. To analyze the discriminatory power and reliability of dermoscopic criteria used in melanoma detection and compare the diagnostic accuracy of existing algorithms. This was a retrospective, observational study of 477 lesions (119 melanomas [24.9%] and 358 nevi [75.1%]), which were divided into 12 image sets that consisted of 39 or 40 images per set. A link on the International Dermoscopy Society website from January 1, 2011, through December 31, 2011, directed participants to the study website. Data analysis was performed from June 1, 2013, through May 31, 2015. Participants included physicians, residents, and medical students, and there were no specialty-type or experience-level restrictions. Participants were randomly assigned to evaluate 1 of the 12 image sets. Associations with melanoma and intraclass correlation coefficients (ICCs) were evaluated for the presence of dermoscopic criteria. Diagnostic accuracy measures were estimated for the following algorithms: the ABCD rule, the Menzies method, the 7-point checklist, the 3-point checklist, chaos and clues, and CASH (color, architecture, symmetry, and homogeneity). A total of 240 participants registered, and 103 (42.9%) evaluated all images. The 110 participants (45.8%) who evaluated fewer than 20 lesions were excluded, resulting in data from 130 participants (54.2%), 121 (93.1%) of whom were regular dermoscopy users. Criteria associated with melanoma included marked architectural disorder (odds ratio [OR], 6.6; 95% CI, 5.6-7.8), pattern asymmetry (OR, 4.9; 95% CI, 4.1-5.8), nonorganized pattern (OR, 3.3; 95% CI, 2.9-3.7), border score of 6 (OR, 3.3; 95% CI, 2.5-4.3), and contour asymmetry (OR, 3.2; 95% CI, 2.7-3.7) (P < .001 for all). Most dermoscopic criteria had poor to fair interobserver agreement. Criteria that reached moderate levels of agreement included comma vessels (ICC, 0.44; 95% CI, 0.40-0.49), absence of vessels (ICC, 0.46; 95% CI, 0.42-0.51), dark brown color (ICC, 0.40; 95% CI, 0.35-0.44), and architectural disorder (ICC, 0.43; 95% CI, 0.39-0.48). The Menzies method had the highest sensitivity for melanoma diagnosis (95.1%) but the lowest specificity (24.8%) compared with any other method (P < .001). The ABCD rule had the highest specificity (59.4%). All methods had similar areas under the receiver operating characteristic curves. Important dermoscopic criteria for melanoma recognition were revalidated by participants with varied experience. Six algorithms tested had similar but modest levels of diagnostic accuracy, and the interobserver agreement of most individual criteria was poor.

Dilution testing using rapid diagnostic tests in a HIV diagnostic algorithm: a novel alternative for confirmation testing in resource limited settings.

PubMed

Shanks, Leslie; Siddiqui, M Ruby; Abebe, Almaz; Piriou, Erwan; Pearce, Neil; Ariti, Cono; Masiga, Johnson; Muluneh, Libsework; Wazome, Joseph; Ritmeijer, Koert; Klarkowski, Derryck

2015-05-14

Current WHO testing guidelines for resource limited settings diagnose HIV on the basis of screening tests without a confirmation test due to cost constraints. This leads to a potential risk of false positive HIV diagnosis. In this paper, we evaluate the dilution test, a novel method for confirmation testing, which is simple, rapid, and low cost. The principle of the dilution test is to alter the sensitivity of a rapid diagnostic test (RDT) by dilution of the sample, in order to screen out the cross reacting antibodies responsible for falsely positive RDT results. Participants were recruited from two testing centres in Ethiopia where a tiebreaker algorithm using 3 different RDTs in series is used to diagnose HIV. All samples positive on the initial screening RDT and every 10th negative sample underwent testing with the gold standard and dilution test. Dilution testing was performed using Determine™ rapid diagnostic test at 6 different dilutions. Results were compared to the gold standard of Western Blot; where Western Blot was indeterminate, PCR testing determined the final result. 2895 samples were recruited to the study. 247 were positive for a prevalence of 8.5 % (247/2895). A total of 495 samples underwent dilution testing. The RDT diagnostic algorithm misclassified 18 samples as positive. Dilution at the level of 1/160 was able to correctly identify all these 18 false positives, but at a cost of a single false negative result (sensitivity 99.6 %, 95 % CI 97.8-100; specificity 100 %, 95 % CI: 98.5-100). Concordance between the gold standard and the 1/160 dilution strength was 99.8 %. This study provides proof of concept for a new, low cost method of confirming HIV diagnosis in resource-limited settings. It has potential for use as a supplementary test in a confirmatory algorithm, whereby double positive RDT results undergo dilution testing, with positive results confirming HIV infection. Negative results require nucleic acid testing to rule out false negative results due to seroconversion or misclassification by the lower sensitivity dilution test. Further research is needed to determine if these results can be replicated in other settings. ClinicalTrials.gov, NCT01716299 .

Noninvasive scoring algorithm to identify significant liver fibrosis among treatment-naive chronic hepatitis C patients.

PubMed

Koller, Tomas; Kollerova, Jana; Huorka, Martin; Meciarova, Iveta; Payer, Juraj

2014-10-01

Staging for liver fibrosis is recommended in the management of hepatitis C as an argument for treatment priority. Our aim was to construct a noninvasive algorithm to predict the significant liver fibrosis (SLF) using common biochemical markers and compare it with some existing models. The study group included 104 consecutive cases; SLF was defined as Ishak fibrosis stage greater than 2. The patient population was assigned randomly to the training and the validation groups of 52 cases each. The training group was used to construct the algorithm from parameters with the best predictive value. Each parameter was assigned a score that was added to the noninvasive fibrosis score (NFS). The accuracy of NFS in predicting SLF was tested in the validation group and compared with APRI, FIB4, and Forns models. Our algorithm used age, alkaline phosphatase, ferritin, APRI, α2 macroglobulin, and insulin and the NFS ranged from -4 to 5. The probability of SLF was 2.6 versus 77.1% in NFS<0 and NFS>0, leaving NFS=0 in a gray zone (29.8% of cases). The area under the receiver operating curve was 0.895 and 0.886, with a specificity, sensitivity, and diagnostic accuracy of 85.1, 92.3, and 87.5% versus 77.8, 100, and 87.9% for the training and the validation group. In comparison, the area under the receiver operating curve for APRI=0.810, FIB4=0.781, and Forns=0.703 with a diagnostic accuracy of 83.9, 72.3, and 62% and gray zone cases in 46.15, 37.5, and 44.2%. We devised an algorithm to calculate the NFS to predict SLF with good accuracy, fewer cases in the gray zone, and a straightforward clinical interpretation. NFS could be used for the initial evaluation of the treatment priority.

Mining peripheral arterial disease cases from narrative clinical notes using natural language processing.

PubMed

Afzal, Naveed; Sohn, Sunghwan; Abram, Sara; Scott, Christopher G; Chaudhry, Rajeev; Liu, Hongfang; Kullo, Iftikhar J; Arruda-Olson, Adelaide M

2017-06-01

Lower extremity peripheral arterial disease (PAD) is highly prevalent and affects millions of individuals worldwide. We developed a natural language processing (NLP) system for automated ascertainment of PAD cases from clinical narrative notes and compared the performance of the NLP algorithm with billing code algorithms, using ankle-brachial index test results as the gold standard. We compared the performance of the NLP algorithm to (1) results of gold standard ankle-brachial index; (2) previously validated algorithms based on relevant International Classification of Diseases, Ninth Revision diagnostic codes (simple model); and (3) a combination of International Classification of Diseases, Ninth Revision codes with procedural codes (full model). A dataset of 1569 patients with PAD and controls was randomly divided into training (n = 935) and testing (n = 634) subsets. We iteratively refined the NLP algorithm in the training set including narrative note sections, note types, and service types, to maximize its accuracy. In the testing dataset, when compared with both simple and full models, the NLP algorithm had better accuracy (NLP, 91.8%; full model, 81.8%; simple model, 83%; P < .001), positive predictive value (NLP, 92.9%; full model, 74.3%; simple model, 79.9%; P < .001), and specificity (NLP, 92.5%; full model, 64.2%; simple model, 75.9%; P < .001). A knowledge-driven NLP algorithm for automatic ascertainment of PAD cases from clinical notes had greater accuracy than billing code algorithms. Our findings highlight the potential of NLP tools for rapid and efficient ascertainment of PAD cases from electronic health records to facilitate clinical investigation and eventually improve care by clinical decision support. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Optical diagnosis of cervical cancer by higher order spectra and boosting

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Mukhopadhyay, Sabyasachi; Barman, Ritwik; Pratiher, Souvik; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-03-01

In this contribution, we report the application of higher order statistical moments using decision tree and ensemble based learning methodology for the development of diagnostic algorithms for optical diagnosis of cancer. The classification results were compared to those obtained with an independent feature extractors like linear discriminant analysis (LDA). The performance and efficacy of these methodology using higher order statistics as a classifier using boosting has higher specificity and sensitivity while being much faster as compared to other time-frequency domain based methods.

First International Diagnosis Competition - DXC'09

NASA Technical Reports Server (NTRS)

Kurtoglu, tolga; Narasimhan, Sriram; Poll, Scott; Garcia, David; Kuhn, Lukas; deKleer, Johan; vanGemund, Arjan; Feldman, Alexander

2009-01-01

A framework to compare and evaluate diagnosis algorithms (DAs) has been created jointly by NASA Ames Research Center and PARC. In this paper, we present the first concrete implementation of this framework as a competition called DXC 09. The goal of this competition was to evaluate and compare DAs in a common platform and to determine a winner based on diagnosis results. 12 DAs (model-based and otherwise) competed in this first year of the competition in 3 tracks that included industrial and synthetic systems. Specifically, the participants provided algorithms that communicated with the run-time architecture to receive scenario data and return diagnostic results. These algorithms were run on extended scenario data sets (different from sample set) to compute a set of pre-defined metrics. A ranking scheme based on weighted metrics was used to declare winners. This paper presents the systems used in DXC 09, description of faults and data sets, a listing of participating DAs, the metrics and results computed from running the DAs, and a superficial analysis of the results.

Effect of Watermarking on Diagnostic Preservation of Atherosclerotic Ultrasound Video in Stroke Telemedicine.

PubMed

Dey, Nilanjan; Bose, Soumyo; Das, Achintya; Chaudhuri, Sheli Sinha; Saba, Luca; Shafique, Shoaib; Nicolaides, Andrew; Suri, Jasjit S

2016-04-01

Embedding of diagnostic and health care information requires secure encryption and watermarking. This research paper presents a comprehensive study for the behavior of some well established watermarking algorithms in frequency domain for the preservation of stroke-based diagnostic parameters. Two different sets of watermarking algorithms namely: two correlation-based (binary logo hiding) and two singular value decomposition (SVD)-based (gray logo hiding) watermarking algorithms are used for embedding ownership logo. The diagnostic parameters in atherosclerotic plaque ultrasound video are namely: (a) bulb identification and recognition which consists of identifying the bulb edge points in far and near carotid walls; (b) carotid bulb diameter; and (c) carotid lumen thickness all along the carotid artery. The tested data set consists of carotid atherosclerotic movies taken under IRB protocol from University of Indiana Hospital, USA-AtheroPoint™ (Roseville, CA, USA) joint pilot study. ROC (receiver operating characteristic) analysis was performed on the bulb detection process that showed an accuracy and sensitivity of 100 % each, respectively. The diagnostic preservation (DPsystem) for SVD-based approach was above 99 % with PSNR (Peak signal-to-noise ratio) above 41, ensuring the retention of diagnostic parameter devalorization as an effect of watermarking. Thus, the fully automated proposed system proved to be an efficient method for watermarking the atherosclerotic ultrasound video for stroke application.

Gearbox vibration diagnostic analyzer

NASA Technical Reports Server (NTRS)

1992-01-01

This report describes the Gearbox Vibration Diagnostic Analyzer installed in the NASA Lewis Research Center's 500 HP Helicopter Transmission Test Stand to monitor gearbox testing. The vibration of the gearbox is analyzed using diagnostic algorithms to calculate a parameter indicating damaged components.

Anatomy-Based Algorithms for Detecting Oral Cancer Using Reflectance and Fluorescence Spectroscopy

PubMed Central

McGee, Sasha; Mardirossian, Vartan; Elackattu, Alphi; Mirkovic, Jelena; Pistey, Robert; Gallagher, George; Kabani, Sadru; Yu, Chung-Chieh; Wang, Zimmern; Badizadegan, Kamran; Grillone, Gregory; Feld, Michael S.

2010-01-01

Objectives We used reflectance and fluorescence spectroscopy to noninvasively and quantitatively distinguish benign from dysplastic/malignant oral lesions. We designed diagnostic algorithms to account for differences in the spectral properties among anatomic sites (gingiva, buccal mucosa, etc). Methods In vivo reflectance and fluorescence spectra were collected from 71 patients with oral lesions. The tissue was then biopsied and the specimen evaluated by histopathology. Quantitative parameters related to tissue morphology and biochemistry were extracted from the spectra. Diagnostic algorithms specific for combinations of sites with similar spectral properties were developed. Results Discrimination of benign from dysplastic/malignant lesions was most successful when algorithms were designed for individual sites (area under the receiver operator characteristic curve [ROC-AUC], 0.75 for the lateral surface of the tongue) and was least accurate when all sites were combined (ROC-AUC, 0.60). The combination of sites with similar spectral properties (floor of mouth and lateral surface of the tongue) yielded an ROC-AUC of 0.71. Conclusions Accurate spectroscopic detection of oral disease must account for spectral variations among anatomic sites. Anatomy-based algorithms for single sites or combinations of sites demonstrated good diagnostic performance in distinguishing benign lesions from dysplastic/malignant lesions and consistently performed better than algorithms developed for all sites combined. PMID:19999369

An ontology-driven, diagnostic modeling system.

PubMed

Haug, Peter J; Ferraro, Jeffrey P; Holmen, John; Wu, Xinzi; Mynam, Kumar; Ebert, Matthew; Dean, Nathan; Jones, Jason

2013-06-01

To present a system that uses knowledge stored in a medical ontology to automate the development of diagnostic decision support systems. To illustrate its function through an example focused on the development of a tool for diagnosing pneumonia. We developed a system that automates the creation of diagnostic decision-support applications. It relies on a medical ontology to direct the acquisition of clinic data from a clinical data warehouse and uses an automated analytic system to apply a sequence of machine learning algorithms that create applications for diagnostic screening. We refer to this system as the ontology-driven diagnostic modeling system (ODMS). We tested this system using samples of patient data collected in Salt Lake City emergency rooms and stored in Intermountain Healthcare's enterprise data warehouse. The system was used in the preliminary development steps of a tool to identify patients with pneumonia in the emergency department. This tool was compared with a manually created diagnostic tool derived from a curated dataset. The manually created tool is currently in clinical use. The automatically created tool had an area under the receiver operating characteristic curve of 0.920 (95% CI 0.916 to 0.924), compared with 0.944 (95% CI 0.942 to 0.947) for the manually created tool. Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement. The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible.

Accuracy of giant African pouched rats for diagnosing tuberculosis: comparison with culture and Xpert® MTB/RIF.

PubMed

Mulder, C; Mgode, G F; Ellis, H; Valverde, E; Beyene, N; Cox, C; Reid, S E; Van't Hoog, A H; Edwards, T L

2017-11-01

Enhanced tuberculosis (TB) case finding using detection rats in Tanzania. To assess the diagnostic accuracy of detection rats compared with culture and Xpert® MTB/RIF, and to compare enhanced case-finding algorithms using rats in smear-negative presumptive TB patients. A fully paired diagnostic accuracy study in which sputum of new adult presumptive TB patients in Tanzania was tested using smear microscopy, 11 detection rats, culture and Xpert. Of 771 eligible participants, 345 (45%) were culture-positive for Mycobacterium tuberculosis, and 264 (34%) were human immunodeficiency virus (HIV) positive. The sensitivity of the detection rats was up to 75.1% (95%CI 70.1-79.5) when compared with culture, and up to 81.8% (95%CI 76.0-86.5) when compared with Xpert, which was statistically significantly higher than the sensitivity of smear microscopy. Corresponding specificity was 40.6% (95%CI 35.9-45.5) compared with culture. The accuracy of rat detection was independent of HIV status. Using rats for triage, followed by Xpert, would result in a statistically higher yield than rats followed by light-emitting diode fluorescence microscopy, whereas the number of false-positives would be significantly lower than when using Xpert alone. Although detection rats did not meet the accuracy criteria as standalone diagnostic or triage testing for presumptive TB, they have additive value as a triage test for enhanced case finding among smear-negative TB patients if more advanced diagnostics are not available.

Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts.

PubMed

Misawa, Masashi; Kudo, Shin-Ei; Mori, Yuichi; Takeda, Kenichi; Maeda, Yasuharu; Kataoka, Shinichi; Nakamura, Hiroki; Kudo, Toyoki; Wakamura, Kunihiko; Hayashi, Takemasa; Katagiri, Atsushi; Baba, Toshiyuki; Ishida, Fumio; Inoue, Haruhiro; Nimura, Yukitaka; Oda, Msahiro; Mori, Kensaku

2017-05-01

Real-time characterization of colorectal lesions during colonoscopy is important for reducing medical costs, given that the need for a pathological diagnosis can be omitted if the accuracy of the diagnostic modality is sufficiently high. However, it is sometimes difficult for community-based gastroenterologists to achieve the required level of diagnostic accuracy. In this regard, we developed a computer-aided diagnosis (CAD) system based on endocytoscopy (EC) to evaluate cellular, glandular, and vessel structure atypia in vivo. The purpose of this study was to compare the diagnostic ability and efficacy of this CAD system with the performances of human expert and trainee endoscopists. We developed a CAD system based on EC with narrow-band imaging that allowed microvascular evaluation without dye (ECV-CAD). The CAD algorithm was programmed based on texture analysis and provided a two-class diagnosis of neoplastic or non-neoplastic, with probabilities. We validated the diagnostic ability of the ECV-CAD system using 173 randomly selected EC images (49 non-neoplasms, 124 neoplasms). The images were evaluated by the CAD and by four expert endoscopists and three trainees. The diagnostic accuracies for distinguishing between neoplasms and non-neoplasms were calculated. ECV-CAD had higher overall diagnostic accuracy than trainees (87.8 vs 63.4%; [Formula: see text]), but similar to experts (87.8 vs 84.2%; [Formula: see text]). With regard to high-confidence cases, the overall accuracy of ECV-CAD was also higher than trainees (93.5 vs 71.7%; [Formula: see text]) and comparable to experts (93.5 vs 90.8%; [Formula: see text]). ECV-CAD showed better diagnostic accuracy than trainee endoscopists and was comparable to that of experts. ECV-CAD could thus be a powerful decision-making tool for less-experienced endoscopists.

Validated methods for identifying tuberculosis patients in health administrative databases: systematic review.

PubMed

Ronald, L A; Ling, D I; FitzGerald, J M; Schwartzman, K; Bartlett-Esquilant, G; Boivin, J-F; Benedetti, A; Menzies, D

2017-05-01

An increasing number of studies are using health administrative databases for tuberculosis (TB) research. However, there are limitations to using such databases for identifying patients with TB. To summarise validated methods for identifying TB in health administrative databases. We conducted a systematic literature search in two databases (Ovid Medline and Embase, January 1980-January 2016). We limited the search to diagnostic accuracy studies assessing algorithms derived from drug prescription, International Classification of Diseases (ICD) diagnostic code and/or laboratory data for identifying patients with TB in health administrative databases. The search identified 2413 unique citations. Of the 40 full-text articles reviewed, we included 14 in our review. Algorithms and diagnostic accuracy outcomes to identify TB varied widely across studies, with positive predictive value ranging from 1.3% to 100% and sensitivity ranging from 20% to 100%. Diagnostic accuracy measures of algorithms using out-patient, in-patient and/or laboratory data to identify patients with TB in health administrative databases vary widely across studies. Use solely of ICD diagnostic codes to identify TB, particularly when using out-patient records, is likely to lead to incorrect estimates of case numbers, given the current limitations of ICD systems in coding TB.

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.

PubMed

Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab

2017-09-01

Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

Pre-operative intra-articular deep tissue sampling with novel retrograde forceps improves the diagnostics in periprosthetic joint infection.

PubMed

Wimmer, Matthias D; Ploeger, Milena M; Friedrich, Max J; Hügle, Thomas; Gravius, Sascha; Randau, Thomas M

2017-07-01

Histopathological tissue analysis is a key parameter within the diagnostic algorithm for suspected periprosthetic joint infections (PJIs), conventionally acquired in open surgery. In 2014, Hügle and co-workers introduced novel retrograde forceps for retrograde synovial biopsy with simultaneous fluid aspiration of the knee joint. We hypothesised that tissue samples acquired by retrograde synovial biopsy are equal to intra-operatively acquired deep representative tissue samples regarding bacterial detection and differentiation of periprosthetic infectious membranes. Thirty patients (male n = 15, 50%; female n = 15, 50%) with 30 suspected PJIs in painful total hip arthroplasties (THAs) were included in this prospective, controlled, non-blinded trial. The results were compared with intra-operatively obtained representative deep tissue samples. In summary, 27 out of 30 patients were diagnosed correctly as infected (17/17) or non-infected (10/13). The sensitivity to predict a PJI using the Retroforce® sampling forceps in addition to standard diagnostics was 85%, the specificity 100%. Retrograde synovial biopsy is a new and rapid diagnostic procedure under local anaesthesia in patients with painful THAs with similar histological results compared to deep tissue sampling.

Colonoscopy video quality assessment using hidden Markov random fields

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dusty; Spofford, Inbar; Vosburgh, Kirby

2011-03-01

With colonoscopy becoming a common procedure for individuals aged 50 or more who are at risk of developing colorectal cancer (CRC), colon video data is being accumulated at an ever increasing rate. However, the clinically valuable information contained in these videos is not being maximally exploited to improve patient care and accelerate the development of new screening methods. One of the well-known difficulties in colonoscopy video analysis is the abundance of frames with no diagnostic information. Approximately 40% - 50% of the frames in a colonoscopy video are contaminated by noise, acquisition errors, glare, blur, and uneven illumination. Therefore, filtering out low quality frames containing no diagnostic information can significantly improve the efficiency of colonoscopy video analysis. To address this challenge, we present a quality assessment algorithm to detect and remove low quality, uninformative frames. The goal of our algorithm is to discard low quality frames while retaining all diagnostically relevant information. Our algorithm is based on a hidden Markov model (HMM) in combination with two measures of data quality to filter out uninformative frames. Furthermore, we present a two-level framework based on an embedded hidden Markov model (EHHM) to incorporate the proposed quality assessment algorithm into a complete, automated diagnostic image analysis system for colonoscopy video.

New method for detection of gastric cancer by hyperspectral imaging: a pilot study

NASA Astrophysics Data System (ADS)

Kiyotoki, Shu; Nishikawa, Jun; Okamoto, Takeshi; Hamabe, Kouichi; Saito, Mari; Goto, Atsushi; Fujita, Yusuke; Hamamoto, Yoshihiko; Takeuchi, Yusuke; Satori, Shin; Sakaida, Isao

2013-02-01

We developed a new, easy, and objective method to detect gastric cancer using hyperspectral imaging (HSI) technology combining spectroscopy and imaging A total of 16 gastroduodenal tumors removed by endoscopic resection or surgery from 14 patients at Yamaguchi University Hospital, Japan, were recorded using a hyperspectral camera (HSC) equipped with HSI technology Corrected spectral reflectance was obtained from 10 samples of normal mucosa and 10 samples of tumors for each case The 16 cases were divided into eight training cases (160 training samples) and eight test cases (160 test samples) We established a diagnostic algorithm with training samples and evaluated it with test samples Diagnostic capability of the algorithm for each tumor was validated, and enhancement of tumors by image processing using the HSC was evaluated The diagnostic algorithm used the 726-nm wavelength, with a cutoff point established from training samples The sensitivity, specificity, and accuracy rates of the algorithm's diagnostic capability in the test samples were 78.8% (63/80), 92.5% (74/80), and 85.6% (137/160), respectively Tumors in HSC images of 13 (81.3%) cases were well enhanced by image processing Differences in spectral reflectance between tumors and normal mucosa suggested that tumors can be clearly distinguished from background mucosa with HSI technology.

A novel algorithm for Bluetooth ECG.

PubMed

Pandya, Utpal T; Desai, Uday B

2012-11-01

In wireless transmission of ECG, data latency will be significant when battery power level and data transmission distance are not maintained. In applications like home monitoring or personalized care, to overcome the joint effect of previous issues of wireless transmission and other ECG measurement noises, a novel filtering strategy is required. Here, a novel algorithm, identified as peak rejection adaptive sampling modified moving average (PRASMMA) algorithm for wireless ECG is introduced. This algorithm first removes error in bit pattern of received data if occurred in wireless transmission and then removes baseline drift. Afterward, a modified moving average is implemented except in the region of each QRS complexes. The algorithm also sets its filtering parameters according to different sampling rate selected for acquisition of signals. To demonstrate the work, a prototyped Bluetooth-based ECG module is used to capture ECG with different sampling rate and in different position of patient. This module transmits ECG wirelessly to Bluetooth-enabled devices where the PRASMMA algorithm is applied on captured ECG. The performance of PRASMMA algorithm is compared with moving average and S-Golay algorithms visually as well as numerically. The results show that the PRASMMA algorithm can significantly improve the ECG reconstruction by efficiently removing the noise and its use can be extended to any parameters where peaks are importance for diagnostic purpose.

Differential Diagnoses of Food-Related Gastrointestinal Symptoms in Patients with Anorexia Nervosa and Bulimia Nervosa: A Review of Literature.

PubMed

Kress, Inge Ulrike; Paslakis, Georgios; Erim, Yesim

2018-03-01

The present review investigates the prevalence and medical causes of food-related gastrointestinal symptoms in eating disorder (ED) patients and recommends a diagnostic algorithm based on the current literature. A literature search was conducted, which included publications from January 2000 until January 2017 Results: Over 90% of ED patients suffer from food-related symptoms. There is no evidence for a higher prevalence of immunological or structural gastrointestinal disorders in ED patients compared to the healthy population. Most food-related symptoms in ED patients are likely to be functional. Diagnostic work-up of food-related symptoms in ED patients needs to be based on clinical history. Only if timing and quality of symptoms point towards a disorder independent from the ED is a comprehensive diagnostic work-up necessary.

Application of Dynamic Logic Algorithm to Inverse Scattering Problems Related to Plasma Diagnostics

NASA Astrophysics Data System (ADS)

Perlovsky, L.; Deming, R. W.; Sotnikov, V.

2010-11-01

In plasma diagnostics scattering of electromagnetic waves is widely used for identification of density and wave field perturbations. In the present work we use a powerful mathematical approach, dynamic logic (DL), to identify the spectra of scattered electromagnetic (EM) waves produced by the interaction of the incident EM wave with a Langmuir soliton in the presence of noise. The problem is especially difficult since the spectral amplitudes of the noise pattern are comparable with the amplitudes of the scattered waves. In the past DL has been applied to a number of complex problems in artificial intelligence, pattern recognition, and signal processing, resulting in revolutionary improvements. Here we demonstrate its application to plasma diagnostic problems. [4pt] Perlovsky, L.I., 2001. Neural Networks and Intellect: using model-based concepts. Oxford University Press, New York, NY.

Vertigo in childhood: proposal for a diagnostic algorithm based upon clinical experience.

PubMed

Casani, A P; Dallan, I; Navari, E; Sellari Franceschini, S; Cerchiai, N

2015-06-01

The aim of this paper is to analyse, after clinical experience with a series of patients with established diagnoses and review of the literature, all relevant anamnestic features in order to build a simple diagnostic algorithm for vertigo in childhood. This study is a retrospective chart review. A series of 37 children underwent complete clinical and instrumental vestibular examination. Only neurological disorders or genetic diseases represented exclusion criteria. All diagnoses were reviewed after applying the most recent diagnostic guidelines. In our experience, the most common aetiology for dizziness is vestibular migraine (38%), followed by acute labyrinthitis/neuritis (16%) and somatoform vertigo (16%). Benign paroxysmal vertigo was diagnosed in 4 patients (11%) and paroxysmal torticollis was diagnosed in a 1-year-old child. In 8% (3 patients) of cases, the dizziness had a post-traumatic origin: 1 canalolithiasis of the posterior semicircular canal and 2 labyrinthine concussions, respectively. Menière's disease was diagnosed in 2 cases. A bilateral vestibular failure of unknown origin caused chronic dizziness in 1 patient. In conclusion, this algorithm could represent a good tool for guiding clinical suspicion to correct diagnostic assessment in dizzy children where no neurological findings are detectable. The algorithm has just a few simple steps, based mainly on two aspects to be investigated early: temporal features of vertigo and presence of hearing impairment. A different algorithm has been proposed for cases in which a traumatic origin is suspected.

Benchmarking Procedures for High-Throughput Context Specific Reconstruction Algorithms

PubMed Central

Pacheco, Maria P.; Pfau, Thomas; Sauter, Thomas

2016-01-01

Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction based on generic genome scale models like ReconX or HMR has the potential to become a diagnostic and treatment tool tailored to the analysis of specific individuals. The respective computational algorithms require a high level of predictive power, robustness and sensitivity. Although multiple context specific reconstruction algorithms were published in the last 10 years, only a fraction of them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding. This review describes and analyses common validation methods used for testing model building algorithms. Two major methods can be distinguished: consistency testing and comparison based testing. The first is concerned with robustness against noise, e.g., missing data due to the impossibility to distinguish between the signal and the background of non-specific binding of probes in a microarray experiment, and whether distinct sets of input expressed genes corresponding to i.e., different tissues yield distinct models. The latter covers methods comparing sets of functionalities, comparison with existing networks or additional databases. We test those methods on several available algorithms and deduce properties of these algorithms that can be compared with future developments. The set of tests performed, can therefore serve as a benchmarking procedure for future algorithms. PMID:26834640

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

PubMed Central

Masood, Ammara; Al-Jumaily, Adel Ali

2013-01-01

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. PMID:24575126

Cost-effectiveness of WHO-Recommended Algorithms for TB Case Finding at Ethiopian HIV Clinics.

PubMed

Adelman, Max W; McFarland, Deborah A; Tsegaye, Mulugeta; Aseffa, Abraham; Kempker, Russell R; Blumberg, Henry M

2018-01-01

The World Health Organization (WHO) recommends active tuberculosis (TB) case finding and a rapid molecular diagnostic test (Xpert MTB/RIF) to detect TB among people living with HIV (PLHIV) in high-burden settings. Information on the cost-effectiveness of these recommended strategies is crucial for their implementation. We conducted a model-based cost-effectiveness analysis comparing 2 algorithms for TB screening and diagnosis at Ethiopian HIV clinics: (1) WHO-recommended symptom screen combined with Xpert for PLHIV with a positive symptom screen and (2) current recommended practice algorithm (CRPA; based on symptom screening, smear microscopy, and clinical TB diagnosis). Our primary outcome was US$ per disability-adjusted life-year (DALY) averted. Secondary outcomes were additional true-positive diagnoses, and false-negative and false-positive diagnoses averted. Compared with CRPA, combining a WHO-recommended symptom screen with Xpert was highly cost-effective (incremental cost of $5 per DALY averted). Among a cohort of 15 000 PLHIV with a TB prevalence of 6% (900 TB cases), this algorithm detected 8 more true-positive cases than CRPA, and averted 2045 false-positive and 8 false-negative diagnoses compared with CRPA. The WHO-recommended algorithm was marginally costlier ($240 000) than CRPA ($239 000). In sensitivity analysis, the symptom screen/Xpert algorithm was dominated at low Xpert sensitivity (66%). In this model-based analysis, combining a WHO-recommended symptom screen with Xpert for TB diagnosis among PLHIV was highly cost-effective ($5 per DALY averted) and more sensitive than CRPA in a high-burden, resource-limited setting.

What is the actual epidemiology of familial hypercholesterolemia in Italy? Evidence from a National Primary Care Database.

PubMed

Guglielmi, Valeria; Bellia, Alfonso; Pecchioli, Serena; Medea, Gerardo; Parretti, Damiano; Lauro, Davide; Sbraccia, Paolo; Federici, Massimo; Cricelli, Iacopo; Cricelli, Claudio; Lapi, Francesco

2016-11-15

There are some inconsistencies on prevalence estimates of familial hypercholesterolemia (FH) in general population across Europe due to variable application of its diagnostic criteria. We aimed to investigate the FH epidemiology in Italy applying the Dutch Lipid Clinical Network (DLCN) score, and two alternative diagnostic algorithms to a primary care database. We performed a retrospective population-based study using the Health Search IMS Health Longitudinal Patient Database (HSD) and including active (alive and currently registered with their general practitioners (GPs)) patients on December 31, 2014. Cases of FH were identified by applying DLCN score. Two further algorithms, based on either ICD9CM coding for FH or some clinical items adopted by the DLCN, were tested towards DLCN itself as gold standard. We estimated a prevalence of 0.01% for "definite" and 0.18% for "definite" plus "probable" cases as per the DLCN. Algorithms 1 and 2 reported a FH prevalence of 0.9 and 0.13%, respectively. Both algorithms resulted in consistent specificity (1: 99.10%; 2: 99.9%) towards DLCN, but Algorithm 2 considerably better identified true positive (sensitivity=85.90%) than Algorithm 1 (sensitivity=10.10%). The application of DLCN or valid diagnostic alternatives in the Italian primary care setting provides estimates of FH prevalence consistent with those reported in other screening studies in Caucasian population. These diagnostic criteria should be therefore fostered among GPs. In the perspective of FH new therapeutic options, the epidemiological picture of FH is even more relevant to foresee the costs and to plan affordable reimbursement programs in Italy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine

NASA Astrophysics Data System (ADS)

Li, Shaoxin; Zhang, Yanjiao; Xu, Junfa; Li, Linfang; Zeng, Qiuyao; Lin, Lin; Guo, Zhouyi; Liu, Zhiming; Xiong, Honglian; Liu, Songhao

2014-09-01

This study aims to present a noninvasive prostate cancer screening methods using serum surface-enhanced Raman scattering (SERS) and support vector machine (SVM) techniques through peripheral blood sample. SERS measurements are performed using serum samples from 93 prostate cancer patients and 68 healthy volunteers by silver nanoparticles. Three types of kernel functions including linear, polynomial, and Gaussian radial basis function (RBF) are employed to build SVM diagnostic models for classifying measured SERS spectra. For comparably evaluating the performance of SVM classification models, the standard multivariate statistic analysis method of principal component analysis (PCA) is also applied to classify the same datasets. The study results show that for the RBF kernel SVM diagnostic model, the diagnostic accuracy of 98.1% is acquired, which is superior to the results of 91.3% obtained from PCA methods. The receiver operating characteristic curve of diagnostic models further confirm above research results. This study demonstrates that label-free serum SERS analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive prostate cancer screening.

Diagnostic Utility of the ADI-R and DSM-5 in the Assessment of Latino Children and Adolescents

ERIC Educational Resources Information Center

Magaña, Sandy; Vanegas, Sandra B.

2017-01-01

Latino children in the US are systematically underdiagnosed with Autism Spectrum Disorder (ASD); therefore, it is important that recent changes to the diagnostic process do not exacerbate this pattern of under-identification. Previous research has found that the Autism Diagnostic Interview-Revised (ADI-R) algorithm, based on the Diagnostic and…

Current challenges in diagnostic imaging of venous thromboembolism.

PubMed

Huisman, Menno V; Klok, Frederikus A

2015-01-01

Because the clinical diagnosis of deep-vein thrombosis and pulmonary embolism is nonspecific, integrated diagnostic approaches for patients with suspected venous thromboembolism have been developed over the years, involving both non-invasive bedside tools (clinical decision rules and D-dimer blood tests) for patients with low pretest probability and diagnostic techniques (compression ultrasound for deep-vein thrombosis and computed tomography pulmonary angiography for pulmonary embolism) for those with a high pretest probability. This combination has led to standardized diagnostic algorithms with proven safety for excluding venous thrombotic disease. At the same time, it has become apparent that, as a result of the natural history of venous thrombosis, there are special patient populations in which the current standard diagnostic algorithms are not sufficient. In this review, we present 3 evidence-based patient cases to underline recent developments in the imaging diagnosis of venous thromboembolism. © 2015 by The American Society of Hematology. All rights reserved.

[Chronic diarrhoea: Definition, classification and diagnosis].

PubMed

Fernández-Bañares, Fernando; Accarino, Anna; Balboa, Agustín; Domènech, Eugeni; Esteve, Maria; Garcia-Planella, Esther; Guardiola, Jordi; Molero, Xavier; Rodríguez-Luna, Alba; Ruiz-Cerulla, Alexandra; Santos, Javier; Vaquero, Eva

2016-10-01

Chronic diarrhoea is a common presenting symptom in both primary care medicine and in specialized gastroenterology clinics. It is estimated that >5% of the population has chronic diarrhoea and nearly 40% of these patients are older than 60 years. Clinicians often need to select the best diagnostic approach to these patients and choose between the multiple diagnostic tests available. In 2014 the Catalan Society of Gastroenterology formed a working group with the main objective of creating diagnostic algorithms based on clinical practice and to evaluate diagnostic tests and the scientific evidence available for their use. The GRADE system was used to classify scientific evidence and strength of recommendations. The consensus document contains 28 recommendations and 6 diagnostic algorithms. The document also describes criteria for referral from primary to specialized care. Copyright © 2015 Elsevier España, S.L.U. y AEEH y AEG. All rights reserved.

The diagnostic management of upper extremity deep vein thrombosis: A review of the literature.

PubMed

Kraaijpoel, Noémie; van Es, Nick; Porreca, Ettore; Büller, Harry R; Di Nisio, Marcello

2017-08-01

Upper extremity deep vein thrombosis (UEDVT) accounts for 4% to 10% of all cases of deep vein thrombosis. UEDVT may present with localized pain, erythema, and swelling of the arm, but may also be detected incidentally by diagnostic imaging tests performed for other reasons. Prompt and accurate diagnosis is crucial to prevent pulmonary embolism and long-term complications as the post-thrombotic syndrome of the arm. Unlike the diagnostic management of deep vein thrombosis (DVT) of the lower extremities, which is well established, the work-up of patients with clinically suspected UEDVT remains uncertain with limited evidence from studies of small size and poor methodological quality. Currently, only one prospective study evaluated the use of an algorithm, similar to the one used for DVT of the lower extremities, for the diagnostic workup of clinically suspected UEDVT. The algorithm combined clinical probability assessment, D-dimer testing and ultrasonography and appeared to safely and effectively exclude UEDVT. However, before recommending its use in routine clinical practice, external validation of this strategy and improvements of the efficiency are needed, especially in high-risk subgroups in whom the performance of the algorithm appeared to be suboptimal, such as hospitalized or cancer patients. In this review, we critically assess the accuracy and efficacy of current diagnostic tools and provide clinical guidance for the diagnostic management of clinically suspected UEDVT. Copyright © 2017 Elsevier Ltd. All rights reserved.

Diagnostic Performance of SRU and ATA Thyroid Nodule Classification Algorithms as Tested With a 1 Million Virtual Thyroid Nodule Model.

PubMed

Boehnke, Mitchell; Patel, Nayana; McKinney, Kristin; Clark, Toshimasa

The Society of Radiologists in Ultrasound (SRU 2005) and American Thyroid Association (ATA 2009 and ATA 2015) have published algorithms regarding thyroid nodule management. Kwak et al. and other groups have described models that estimate thyroid nodules' malignancy risk. The aim of our study is to use Kwak's model to evaluate the tradeoffs of both sensitivity and specificity of SRU 2005, ATA 2009 and ATA 2015 management algorithms. 1,000,000 thyroid nodules were modeled in MATLAB. Ultrasound characteristics were modeled after published data. Malignancy risk was estimated per Kwak's model and assigned as a binary variable. All nodules were then assessed using the published management algorithms. With the malignancy variable as condition positivity and algorithms' recommendation for FNA as test positivity, diagnostic performance was calculated. Modeled nodule characteristics mimic those of Kwak et al. 12.8% nodules were assigned as malignant (malignancy risk range of 2.0-98%). FNA was recommended for 41% of nodules by SRU 2005, 66% by ATA 2009, and 82% by ATA 2015. Sensitivity and specificity is significantly different (< 0.0001): 49% and 60% for SRU; 81% and 36% for ATA 2009; and 95% and 20% for ATA 2015. SRU 2005, ATA 2009 and ATA 2015 algorithms are used routinely in clinical practice to determine whether thyroid nodule biopsy is indicated. We demonstrate significant differences in these algorithms' diagnostic performance, which result in a compromise between sensitivity and specificity. Copyright © 2017 Elsevier Inc. All rights reserved.

Differentiation of thrombus from pannus as the cause of acquired mechanical prosthetic heart valve obstruction by non-invasive imaging: a review of the literature.

PubMed

Tanis, Wilco; Habets, Jesse; van den Brink, Renee B A; Symersky, Petr; Budde, Ricardo P J; Chamuleau, Steven A J

2014-02-01

For acquired mechanical prosthetic heart valve (PHV) obstruction and suspicion on thrombosis, recently updated European Society of Cardiology guidelines advocate the confirmation of thrombus by transthoracic echocardiography, transesophageal echocardiography (TEE), and fluoroscopy. However, no evidence-based diagnostic algorithm is available for correct thrombus detection, although this is clinically important as fibrinolysis is contraindicated in non-thrombotic obstruction (isolated pannus). Here, we performed a review of the literature in order to propose a diagnostic algorithm. We performed a systematic search in Pubmed and Embase. Included publications were assessed on methodological quality based on the validated Quality Assessment of Diagnostic Accuracy Studies (QUADAS) II checklist. Studies were scarce (n = 15) and the majority were of moderate methodological quality. In total, 238 mechanical PHV's with acquired obstruction and a reliable reference standard were included for the evaluation of the role of fluoroscopy, echocardiography, or multidetector-row computed tomography (MDCT). In acquired PHV obstruction caused by thrombosis, mass detection by TEE and leaflet restriction detected by fluoroscopy were observed in the majority of cases (96 and 100%, respectively). In contrast, in acquired PHV obstruction free of thrombosis (pannus), leaflet restriction detected by fluoroscopy was absent in some cases (17%) and mass detection by TEE was absent in the majority of cases (66%). In case of mass detection by TEE, predictors for obstructive thrombus masses (compared with pannus masses) were leaflet restriction, soft echo density, and increased mass length. In situations of inconclusive echocardiography, MDCT may correctly detect pannus/thrombus based on the morphological aspects and localization. In acquired mechanical PHV obstruction without leaflet restriction and absent mass on TEE, obstructive PHV thrombosis cannot be confirmed and consequently, fibrinolysis is not advised. Based on the literature search and our opinion, a diagnostic algorithm is provided to correctly identify non-thrombotic PHV obstruction, which is highly relevant in daily clinical practice.

Clinical validation of a novel diagnostic HIV-2 total nucleic acid qualitative assay using the Abbott m2000 platform: Implications for complementary HIV-2 nucleic acid testing for the CDC 4th generation HIV diagnostic testing algorithm.

PubMed

Chang, Ming; Wong, Audrey J S; Raugi, Dana N; Smith, Robert A; Seilie, Annette M; Ortega, Jose P; Bogusz, Kyle M; Sall, Fatima; Ba, Selly; Seydi, Moussa; Gottlieb, Geoffrey S; Coombs, Robert W

2017-01-01

The 2014 CDC 4th generation HIV screening algorithm includes an orthogonal immunoassay to confirm and discriminate HIV-1 and HIV-2 antibodies. Additional nucleic acid testing (NAT) is recommended to resolve indeterminate or undifferentiated HIV seroreactivity. HIV-2 NAT requires a second-line assay to detect HIV-2 total nucleic acid (TNA) in patients' blood cells, as a third of untreated patients have undetectable plasma HIV-2 RNA. To validate a qualitative HIV-2 TNA assay using peripheral blood mononuclear cells (PBMC) from HIV-2-infected Senegalese study participants. We evaluated the assay precision, sensitivity, specificity, and diagnostic performance of an HIV-2 TNA assay. Matched plasma and PBMC samples were collected from 25 HIV-1, 30 HIV-2, 8 HIV-1/-2 dual-seropositive and 25 HIV seronegative individuals. Diagnostic performance was evaluated by comparing the outcome of the TNA assay to the results obtained by the 4th generation HIV screening and confirmatory immunoassays. All PBMC from 30 HIV-2 seropositive participants tested positive for HIV-2 TNA including 23 patients with undetectable plasma RNA. Of the 30 matched plasma specimens, one was HIV non-reactive. Samples from 50 non-HIV-2 infected individuals were confirmed as non-reactive for HIV-2 Ab and negative for HIV-2 TNA. The agreement between HIV-2 TNA and the combined immunoassay results was 98.8% (79/80). Furthermore, HIV-2 TNA was detected in 7 of 8 PBMC specimens from HIV-1/HIV-2 dual-seropositive participants. Our TNA assay detected HIV-2 DNA/RNA in PBMC from serologically HIV-2 reactive, HIV indeterminate or HIV undifferentiated individuals with undetectable plasma RNA, and is suitable for confirming HIV-2 infection in the HIV testing algorithm. Copyright © 2016 Elsevier B.V. All rights reserved.

Validation of a new algorithm for a quick and easy RT-PCR-based ALK test in a large series of lung adenocarcinomas: Comparison with FISH, immunohistochemistry and next generation sequencing assays.

PubMed

Marchetti, Antonio; Pace, Maria Vittoria; Di Lorito, Alessia; Canarecci, Sara; Felicioni, Lara; D'Antuono, Tommaso; Liberatore, Marcella; Filice, Giampaolo; Guetti, Luigi; Mucilli, Felice; Buttitta, Fiamma

2016-09-01

Anaplastic Lymphoma Kinase (ALK) gene rearrangements have been described in 3-5% of lung adenocarcinomas (ADC) and their identification is essential to select patients for treatment with ALK tyrosine kinase inhibitors. For several years, fluorescent in situ hybridization (FISH) has been considered as the only validated diagnostic assay. Currently, alternative methods are commercially available as diagnostic tests. A series of 217 ADC comprising 196 consecutive resected tumors and 21 ALK FISH-positive cases from an independent series of 702 ADC were investigated. All specimens were screened by IHC (ALK-D5F3-CDx-Ventana), FISH (Vysis ALK Break-Apart-Abbott) and RT-PCR (ALK RGQ RT-PCR-Qiagen). Results were compared and discordant cases subjected to Next Generation Sequencing. Thirty-nine of 217 samples were positive by the ALK RGQ RT-PCR assay, using a threshold cycle (Ct) cut-off ≤35.9, as recommended. Of these positive samples, 14 were negative by IHC and 12 by FISH. ALK RGQ RT-PCR/FISH discordant cases were analyzed by the NGS assay with results concordant with FISH data. In order to obtain the maximum level of agreement between FISH and ALK RGQ RT-PCR data, we introduced a new scoring algorithm based on the ΔCt value. A ΔCt cut-off level ≤3.5 was used in a pilot series. Then the algorithm was tested on a completely independent validation series. By using the new scoring algorithm and FISH as reference standard, the sensitivity and the specificity of the ALK RGQ RT-PCR(ΔCt) assay were 100% and 100%, respectively. Our results suggest that the ALK RGQ RT-PCR test could be useful in clinical practice as a complementary assay in multi-test diagnostic algorithms or even, if our data will be confirmed in independent studies, as a standalone or screening test for the selection of patients to be treated with ALK inhibitors. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Artificial intelligence techniques applied to the development of a decision–support system for diagnosing celiac disease

PubMed Central

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2013-01-01

Background Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. Objective To develop a clinical decision–support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. Methods A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. Results The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision–support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k = 0.68 (p < 0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician’s diagnostic impression and the gold standard k = 0. 64 (p < 0.0001). There was moderate agreement between the physician’s diagnostic impression and CDSS k = 0.46 (p = 0.0008). Conclusions The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. PMID:21917512

Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease.

PubMed

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2011-11-01

Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. To develop a clinical decision-support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision-support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k=0.68 (p<0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician's diagnostic impression and the gold standard k=0. 64 (p<0.0001). There was moderate agreement between the physician's diagnostic impression and CDSS k=0.46 (p=0.0008). The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Chest CT window settings with multiscale adaptive histogram equalization: pilot study.

PubMed

Fayad, Laura M; Jin, Yinpeng; Laine, Andrew F; Berkmen, Yahya M; Pearson, Gregory D; Freedman, Benjamin; Van Heertum, Ronald

2002-06-01

Multiscale adaptive histogram equalization (MAHE), a wavelet-based algorithm, was investigated as a method of automatic simultaneous display of the full dynamic contrast range of a computed tomographic image. Interpretation times were significantly lower for MAHE-enhanced images compared with those for conventionally displayed images. Diagnostic accuracy, however, was insufficient in this pilot study to allow recommendation of MAHE as a replacement for conventional window display.

A Comparison of Vibration and Oil Debris Gear Damage Detection Methods Applied to Pitting Damage

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.

2000-01-01

Helicopter Health Usage Monitoring Systems (HUMS) must provide reliable, real-time performance monitoring of helicopter operating parameters to prevent damage of flight critical components. Helicopter transmission diagnostics are an important part of a helicopter HUMS. In order to improve the reliability of transmission diagnostics, many researchers propose combining two technologies, vibration and oil monitoring, using data fusion and intelligent systems. Some benefits of combining multiple sensors to make decisions include improved detection capabilities and increased probability the event is detected. However, if the sensors are inaccurate, or the features extracted from the sensors are poor predictors of transmission health, integration of these sensors will decrease the accuracy of damage prediction. For this reason, one must verify the individual integrity of vibration and oil analysis methods prior to integrating the two technologies. This research focuses on comparing the capability of two vibration algorithms, FM4 and NA4, and a commercially available on-line oil debris monitor to detect pitting damage on spur gears in the NASA Glenn Research Center Spur Gear Fatigue Test Rig. Results from this research indicate that the rate of change of debris mass measured by the oil debris monitor is comparable to the vibration algorithms in detecting gear pitting damage.

Spectral areas and ratios classifier algorithm for pancreatic tissue classification using optical spectroscopy

NASA Astrophysics Data System (ADS)

Chandra, Malavika; Scheiman, James; Simeone, Diane; McKenna, Barbara; Purdy, Julianne; Mycek, Mary-Ann

2010-01-01

Pancreatic adenocarcinoma is one of the leading causes of cancer death, in part because of the inability of current diagnostic methods to reliably detect early-stage disease. We present the first assessment of the diagnostic accuracy of algorithms developed for pancreatic tissue classification using data from fiber optic probe-based bimodal optical spectroscopy, a real-time approach that would be compatible with minimally invasive diagnostic procedures for early cancer detection in the pancreas. A total of 96 fluorescence and 96 reflectance spectra are considered from 50 freshly excised tissue sites-including human pancreatic adenocarcinoma, chronic pancreatitis (inflammation), and normal tissues-on nine patients. Classification algorithms using linear discriminant analysis are developed to distinguish among tissues, and leave-one-out cross-validation is employed to assess the classifiers' performance. The spectral areas and ratios classifier (SpARC) algorithm employs a combination of reflectance and fluorescence data and has the best performance, with sensitivity, specificity, negative predictive value, and positive predictive value for correctly identifying adenocarcinoma being 85, 89, 92, and 80%, respectively.

Accurate ECG diagnosis of atrial tachyarrhythmias using quantitative analysis: a prospective diagnostic and cost-effectiveness study.

PubMed

Krummen, David E; Patel, Mitul; Nguyen, Hong; Ho, Gordon; Kazi, Dhruv S; Clopton, Paul; Holland, Marian C; Greenberg, Scott L; Feld, Gregory K; Faddis, Mitchell N; Narayan, Sanjiv M

2010-11-01

Quantitative ECG Analysis. Optimal atrial tachyarrhythmia management is facilitated by accurate electrocardiogram interpretation, yet typical atrial flutter (AFl) may present without sawtooth F-waves or RR regularity, and atrial fibrillation (AF) may be difficult to separate from atypical AFl or rapid focal atrial tachycardia (AT). We analyzed whether improved diagnostic accuracy using a validated analysis tool significantly impacts costs and patient care. We performed a prospective, blinded, multicenter study using a novel quantitative computerized algorithm to identify atrial tachyarrhythmia mechanism from the surface ECG in patients referred for electrophysiology study (EPS). In 122 consecutive patients (age 60 ± 12 years) referred for EPS, 91 sustained atrial tachyarrhythmias were studied. ECGs were also interpreted by 9 physicians from 3 specialties for comparison and to allow healthcare system modeling. Diagnostic accuracy was compared to the diagnosis at EPS. A Markov model was used to estimate the impact of improved arrhythmia diagnosis. We found 13% of typical AFl ECGs had neither sawtooth flutter waves nor RR regularity, and were misdiagnosed by the majority of clinicians (0/6 correctly diagnosed by consensus visual interpretation) but correctly by quantitative analysis in 83% (5/6, P = 0.03). AF diagnosis was also improved through use of the algorithm (92%) versus visual interpretation (primary care: 76%, P < 0.01). Economically, we found that these improvements in diagnostic accuracy resulted in an average cost-savings of $1,303 and 0.007 quality-adjusted-life-years per patient. Typical AFl and AF are frequently misdiagnosed using visual criteria. Quantitative analysis improves diagnostic accuracy and results in improved healthcare costs and patient outcomes. © 2010 Wiley Periodicals, Inc.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge.

PubMed

Bron, Esther E; Smits, Marion; van der Flier, Wiesje M; Vrenken, Hugo; Barkhof, Frederik; Scheltens, Philip; Papma, Janne M; Steketee, Rebecca M E; Méndez Orellana, Carolina; Meijboom, Rozanna; Pinto, Madalena; Meireles, Joana R; Garrett, Carolina; Bastos-Leite, António J; Abdulkadir, Ahmed; Ronneberger, Olaf; Amoroso, Nicola; Bellotti, Roberto; Cárdenas-Peña, David; Álvarez-Meza, Andrés M; Dolph, Chester V; Iftekharuddin, Khan M; Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir S; Franke, Katja; Gaser, Christian; Ledig, Christian; Guerrero, Ricardo; Tong, Tong; Gray, Katherine R; Moradi, Elaheh; Tohka, Jussi; Routier, Alexandre; Durrleman, Stanley; Sarica, Alessia; Di Fatta, Giuseppe; Sensi, Francesco; Chincarini, Andrea; Smith, Garry M; Stoyanov, Zhivko V; Sørensen, Lauge; Nielsen, Mads; Tangaro, Sabina; Inglese, Paolo; Wachinger, Christian; Reuter, Martin; van Swieten, John C; Niessen, Wiro J; Klein, Stefan

2015-05-01

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org. Copyright © 2015 Elsevier Inc. All rights reserved.

Greater Prevalence of Proposed ICD-11 Alcohol and Cannabis Dependence Compared to ICD-10, DSM-IV, and DSM-5 in Treated Adolescents.

PubMed

Chung, Tammy; Cornelius, Jack; Clark, Duncan; Martin, Christopher

2017-09-01

Proposed International Classification of Diseases, 11th edition (ICD-11), criteria for substance use disorder (SUD) radically simplify the algorithm used to diagnose substance dependence. Major differences in case identification across DSM and ICD impact determinations of treatment need and conceptualizations of substance dependence. This study compared the draft algorithm for ICD-11 SUD against DSM-IV, DSM-5, and ICD-10, for alcohol and cannabis. Adolescents (n = 339, ages 14 to 18) admitted to intensive outpatient addictions treatment completed, as part of a research study, a Structured Clinical Interview for DSM SUDs adapted for use with adolescents and which has been used to assess DSM and ICD SUD diagnoses. Analyses examined prevalence across classification systems, diagnostic concordance, and sources of diagnostic disagreement. Prevalence of any past-year proposed ICD-11 alcohol or cannabis use disorder was significantly lower compared to DSM-IV and DSM-5 (ps < 0.01). However, prevalence of proposed ICD-11 alcohol and cannabis dependence diagnoses was significantly higher compared to DSM-IV, DSM-5, and ICD-10 (ps < 0.01). ICD-11 and DSM-5 SUD diagnoses showed only moderate concordance. For both alcohol and cannabis, youth typically met criteria for an ICD-11 dependence diagnosis by reporting tolerance and much time spent using or recovering from the substance, rather than symptoms indicating impaired control over use. The proposed ICD-11 dependence algorithm appears to "overdiagnose" dependence on alcohol and cannabis relative to DSM-IV and ICD-10 dependence, and DSM-5 moderate/severe use disorder, generating potential "false-positive" cases of dependence. Among youth who met criteria for proposed ICD-11 dependence, few reported impaired control over substance use, highlighting ongoing issues in the conceptualization and diagnosis of SUD. Copyright © 2017 by the Research Society on Alcoholism.

Economic evaluation of laboratory testing strategies for hospital-associated Clostridium difficile infection.

PubMed

Schroeder, Lee F; Robilotti, Elizabeth; Peterson, Lance R; Banaei, Niaz; Dowdy, David W

2014-02-01

Clostridium difficile infection (CDI) is the most common cause of infectious diarrhea in health care settings, and for patients presumed to have CDI, their isolation while awaiting laboratory results is costly. Newer rapid tests for CDI may reduce this burden, but the economic consequences of different testing algorithms remain unexplored. We used decision analysis from the hospital perspective to compare multiple CDI testing algorithms for adult inpatients with suspected CDI, assuming patient management according to laboratory results. CDI testing strategies included combinations of on-demand PCR (odPCR), batch PCR, lateral-flow diagnostics, plate-reader enzyme immunoassay, and direct tissue culture cytotoxicity. In the reference scenario, algorithms incorporating rapid testing were cost-effective relative to nonrapid algorithms. For every 10,000 symptomatic adults, relative to a strategy of treating nobody, lateral-flow glutamate dehydrogenase (GDH)/odPCR generated 831 true-positive results and cost $1,600 per additional true-positive case treated. Stand-alone odPCR was more effective and more expensive, identifying 174 additional true-positive cases at $6,900 per additional case treated. All other testing strategies were dominated by (i.e., more costly and less effective than) stand-alone odPCR or odPCR preceded by lateral-flow screening. A cost-benefit analysis (including estimated costs of missed cases) favored stand-alone odPCR in most settings but favored odPCR preceded by lateral-flow testing if a missed CDI case resulted in less than $5,000 of extended hospital stay costs and <2 transmissions, if lateral-flow GDH diagnostic sensitivity was >93%, or if the symptomatic carrier proportion among the toxigenic culture-positive cases was >80%. These results can aid guideline developers and laboratory directors who are considering rapid testing algorithms for diagnosing CDI.

Economic Evaluation of Laboratory Testing Strategies for Hospital-Associated Clostridium difficile Infection

PubMed Central

Robilotti, Elizabeth; Peterson, Lance R.; Banaei, Niaz; Dowdy, David W.

2014-01-01

Clostridium difficile infection (CDI) is the most common cause of infectious diarrhea in health care settings, and for patients presumed to have CDI, their isolation while awaiting laboratory results is costly. Newer rapid tests for CDI may reduce this burden, but the economic consequences of different testing algorithms remain unexplored. We used decision analysis from the hospital perspective to compare multiple CDI testing algorithms for adult inpatients with suspected CDI, assuming patient management according to laboratory results. CDI testing strategies included combinations of on-demand PCR (odPCR), batch PCR, lateral-flow diagnostics, plate-reader enzyme immunoassay, and direct tissue culture cytotoxicity. In the reference scenario, algorithms incorporating rapid testing were cost-effective relative to nonrapid algorithms. For every 10,000 symptomatic adults, relative to a strategy of treating nobody, lateral-flow glutamate dehydrogenase (GDH)/odPCR generated 831 true-positive results and cost $1,600 per additional true-positive case treated. Stand-alone odPCR was more effective and more expensive, identifying 174 additional true-positive cases at $6,900 per additional case treated. All other testing strategies were dominated by (i.e., more costly and less effective than) stand-alone odPCR or odPCR preceded by lateral-flow screening. A cost-benefit analysis (including estimated costs of missed cases) favored stand-alone odPCR in most settings but favored odPCR preceded by lateral-flow testing if a missed CDI case resulted in less than $5,000 of extended hospital stay costs and <2 transmissions, if lateral-flow GDH diagnostic sensitivity was >93%, or if the symptomatic carrier proportion among the toxigenic culture-positive cases was >80%. These results can aid guideline developers and laboratory directors who are considering rapid testing algorithms for diagnosing CDI. PMID:24478478

[Diagnostic work-up of pulmonary nodules : Management of pulmonary nodules detected with low‑dose CT screening].

PubMed

Wormanns, D

2016-09-01

Pulmonary nodules are the most frequent pathological finding in low-dose computed tomography (CT) scanning for early detection of lung cancer. Early stages of lung cancer are often manifested as pulmonary nodules; however, the very commonly occurring small nodules are predominantly benign. These benign nodules are responsible for the high percentage of false positive test results in screening studies. Appropriate diagnostic algorithms are necessary to reduce false positive screening results and to improve the specificity of lung cancer screening. Such algorithms are based on some of the basic principles comprehensively described in this article. Firstly, the diameter of nodules allows a differentiation between large (>8 mm) probably malignant and small (<8 mm) probably benign nodules. Secondly, some morphological features of pulmonary nodules in CT can prove their benign nature. Thirdly, growth of small nodules is the best non-invasive predictor of malignancy and is utilized as a trigger for further diagnostic work-up. Non-invasive testing using positron emission tomography (PET) and contrast enhancement as well as invasive diagnostic tests (e.g. various procedures for cytological and histological diagnostics) are briefly described in this article. Different nodule morphology using CT (e.g. solid and semisolid nodules) is associated with different biological behavior and different algorithms for follow-up are required. Currently, no obligatory algorithm is available in German-speaking countries for the management of pulmonary nodules, which reflects the current state of knowledge. The main features of some international and American recommendations are briefly presented in this article from which conclusions for the daily clinical use are derived.

Cost-effective Diagnostic Checklists for Meningitis in Resource Limited Settings

PubMed Central

Durski, Kara N.; Kuntz, Karen M.; Yasukawa, Kosuke; Virnig, Beth A.; Meya, David B.; Boulware, David R.

2013-01-01

Background Checklists can standardize patient care, reduce errors, and improve health outcomes. For meningitis in resource-limited settings, with high patient loads and limited financial resources, CNS diagnostic algorithms may be useful to guide diagnosis and treatment. However, the cost-effectiveness of such algorithms is unknown. Methods We used decision analysis methodology to evaluate the costs, diagnostic yield, and cost-effectiveness of diagnostic strategies for adults with suspected meningitis in resource limited settings with moderate/high HIV prevalence. We considered three strategies: 1) comprehensive “shotgun” approach of utilizing all routine tests; 2) “stepwise” strategy with tests performed in a specific order with additional TB diagnostics; 3) “minimalist” strategy of sequential ordering of high-yield tests only. Each strategy resulted in one of four meningitis diagnoses: bacterial (4%), cryptococcal (59%), TB (8%), or other (aseptic) meningitis (29%). In model development, we utilized prevalence data from two Ugandan sites and published data on test performance. We validated the strategies with data from Malawi, South Africa, and Zimbabwe. Results The current comprehensive testing strategy resulted in 93.3% correct meningitis diagnoses costing $32.00/patient. A stepwise strategy had 93.8% correct diagnoses costing an average of $9.72/patient, and a minimalist strategy had 91.1% correct diagnoses costing an average of $6.17/patient. The incremental cost effectiveness ratio was $133 per additional correct diagnosis for the stepwise over minimalist strategy. Conclusions Through strategically choosing the order and type of testing coupled with disease prevalence rates, algorithms can deliver more care more efficiently. The algorithms presented herein are generalizable to East Africa and Southern Africa. PMID:23466647

Validity and Reliability of Dermoscopic Criteria Used to Differentiate Nevi From Melanoma

PubMed Central

Carrera, Cristina; Marchetti, Michael A.; Dusza, StephenW.; Argenziano, Giuseppe; Braun, Ralph P.; Halpern, Allan C.; Jaimes, Natalia; Kittler, Harald J.; Malvehy, Josep; Menzies, Scott W.; Pellacani, Giovanni; Puig, Susana; Rabinovitz, Harold S.; Scope, Alon; Soyer, H. Peter; Stolz, Wilhelm; Hofmann-Wellenhof, Rainer; Zalaudek, Iris; Marghoob, Ashfaq A.

2017-01-01

IMPORTANCE The comparative diagnostic performance of dermoscopic algorithms and their individual criteria are not well studied. OBJECTIVES To analyze the discriminatory power and reliability of dermoscopic criteria used in melanoma detection and compare the diagnostic accuracy of existing algorithms. DESIGN, SETTING, AND PARTICIPANTS This was a retrospective, observational study of 477 lesions (119 melanomas [24.9%] and 358 nevi [75.1%]), which were divided into 12 image sets that consisted of 39 or 40 images per set. A link on the International Dermoscopy Society website from January 1, 2011, through December 31, 2011, directed participants to the study website. Data analysis was performed from June 1, 2013, through May 31, 2015. Participants included physicians, residents, and medical students, and there were no specialty-type or experience-level restrictions. Participants were randomly assigned to evaluate 1 of the 12 image sets. MAIN OUTCOMES AND MEASURES Associations with melanoma and intraclass correlation coefficients (ICCs) were evaluated for the presence of dermoscopic criteria. Diagnostic accuracy measures were estimated for the following algorithms: the ABCD rule, the Menzies method, the 7-point checklist, the 3-point checklist, chaos and clues, and CASH (color, architecture, symmetry, and homogeneity). RESULTS A total of 240 participants registered, and 103 (42.9%) evaluated all images. The 110 participants (45.8%) who evaluated fewer than 20 lesions were excluded, resulting in data from 130 participants (54.2%), 121 (93.1%) of whom were regular dermoscopy users. Criteria associated with melanoma included marked architectural disorder (odds ratio [OR], 6.6; 95% CI, 5.6–7.8), pattern asymmetry (OR, 4.9; 95% CI, 4.1–5.8), nonorganized pattern (OR, 3.3; 95% CI, 2.9–3.7), border score of 6 (OR, 3.3; 95% CI, 2.5–4.3), and contour asymmetry (OR, 3.2; 95% CI, 2.7–3.7) (P < .001 for all). Most dermoscopic criteria had poor to fair interobserver agreement. Criteria that reached moderate levels of agreement included comma vessels (ICC, 0.44; 95% CI, 0.40–0.49), absence of vessels (ICC, 0.46; 95% CI, 0.42–0.51), dark brown color (ICC, 0.40; 95% CI, 0.35–0.44), and architectural disorder (ICC, 0.43; 95% CI, 0.39–0.48). The Menzies method had the highest sensitivity for melanoma diagnosis (95.1%) but the lowest specificity (24.8%) compared with any other method (P < .001). The ABCD rule had the highest specificity (59.4%). All methods had similar areas under the receiver operating characteristic curves. CONCLUSIONS AND RELEVANCE Important dermoscopic criteria for melanoma recognition were revalidated by participants with varied experience. Six algorithms tested had similar but modest levels of diagnostic accuracy, and the interobserver agreement of most individual criteria was poor. PMID:27074267

A utility/cost analysis of breast cancer risk prediction algorithms

NASA Astrophysics Data System (ADS)

Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

2016-03-01

Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography.

PubMed

Coy, Heidi; Young, Jonathan R; Douek, Michael L; Brown, Matthew S; Sayre, James; Raman, Steven S

2017-07-01

To evaluate the performance of a novel, quantitative computer-aided diagnostic (CAD) algorithm on four-phase multidetector computed tomography (MDCT) to detect peak lesion attenuation to enable differentiation of clear cell renal cell carcinoma (ccRCC) from chromophobe RCC (chRCC), papillary RCC (pRCC), oncocytoma, and fat-poor angiomyolipoma (fp-AML). We queried our clinical databases to obtain a cohort of histologically proven renal masses with preoperative MDCT with four phases [unenhanced (U), corticomedullary (CM), nephrographic (NP), and excretory (E)]. A whole lesion 3D contour was obtained in all four phases. The CAD algorithm determined a region of interest (ROI) of peak lesion attenuation within the 3D lesion contour. For comparison, a manual ROI was separately placed in the most enhancing portion of the lesion by visual inspection for a reference standard, and in uninvolved renal cortex. Relative lesion attenuation for both CAD and manual methods was obtained by normalizing the CAD peak lesion attenuation ROI (and the reference standard manually placed ROI) to uninvolved renal cortex with the formula [(peak lesion attenuation ROI - cortex ROI)/cortex ROI] × 100%. ROC analysis and area under the curve (AUC) were used to assess diagnostic performance. Bland-Altman analysis was used to compare peak ROI between CAD and manual method. The study cohort comprised 200 patients with 200 unique renal masses: 106 (53%) ccRCC, 32 (16%) oncocytomas, 18 (9%) chRCCs, 34 (17%) pRCCs, and 10 (5%) fp-AMLs. In the CM phase, CAD-derived ROI enabled characterization of ccRCC from chRCC, pRCC, oncocytoma, and fp-AML with AUCs of 0.850 (95% CI 0.732-0.968), 0.959 (95% CI 0.930-0.989), 0.792 (95% CI 0.716-0.869), and 0.825 (95% CI 0.703-0.948), respectively. On Bland-Altman analysis, there was excellent agreement of CAD and manual methods with mean differences between 14 and 26 HU in each phase. A novel, quantitative CAD algorithm enabled robust peak HU lesion detection and discrimination of ccRCC from other renal lesions with similar performance compared to the manual method.

Comparing the dimensional structure and diagnostic algorithms between DSM-5 and ICD-11 PTSD in children and adolescents.

PubMed

Sachser, Cedric; Berliner, Lucy; Holt, Tonje; Jensen, Tine; Jungbluth, Nathaniel; Risch, Elizabeth; Rosner, Rita; Goldbeck, Lutz

2018-02-01

In contrast to the DSM-5, which expanded the posttraumatic stress disorder (PTSD) symptom profile to 20 symptoms, a workgroup of the upcoming ICD-11 suggested a reduced symptom profile with six symptoms for PTSD. Therefore, the objective of the study was to investigate the dimensional structure of DSM-5 and ICD-11 PTSD in a clinical sample of trauma-exposed children and adolescents and to compare the diagnostic rates of PTSD between diagnostic systems. The study sample consisted of 475 self-reports and 424 caregiver-reports on the child and adolescent trauma screen (CATS), which were collected at pediatric mental health clinics in the US, Norway and Germany. The factor structure of the PTSD construct as defined in the DSM-5 and in alternative models of both DSM-5 and ICD-11 was investigated using confirmatory factor analyses (CFA). To evaluate differences in PTSD prevalence, McNemar's tests for correlated proportions were used. CFA results demonstrated excellent model fit for the proposed ICD-11 model of PTSD. For the DSM-5 models we found the best fit for the hybrid model. Diagnostic rates were significantly lower according to ICD-11 (self-report: 23.4%; caregiver-report: 16.5%) compared with the DSM-5 (self-report: 37.8%; caregiver-report: 31.8%). Agreement was low between diagnostic systems. Study findings provide support for an alternative latent dimensionality of DSM-5 PTSD in children and adolescents. The conceptualization of ICD-11 PTSD shows an excellent fit. Inconsistent PTSD constructs and significantly diverging diagnostic rates between DSM-5 and the ICD-11 will result in major challenges for researchers and clinicians in the field of psychotraumatology.

A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects

PubMed Central

Ng, Selina S. Y.; Tse, Peter W.; Tsui, Kwok L.

2014-01-01

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets. PMID:24419162

A one-versus-all class binarization strategy for bearing diagnostics of concurrent defects.

PubMed

Ng, Selina S Y; Tse, Peter W; Tsui, Kwok L

2014-01-13

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets.

Advances in mechanisms, diagnosis, and treatment of pernicious anemia.

PubMed

Rojas Hernandez, Cristhiam M; Oo, Thein Hlaing

2015-03-01

Pernicious anemia (PA) is an entity initially described in 1849 as a condition that consisted of pallor, weakness, and progressive health decline. Since then several advances led to the conclusion that PA is an autoimmune disease characterized by the deficient absorption of dietary cobalamin. It is currently recognized as the most common cause of cobalamin deficiency worldwide. We hereby review the current understanding of the disease and its neurological, hematological, and biochemical manifestations with emphasis on the diagnostic approach, treatment, and monitoring strategies. We propose an algorithm for the diagnostic approach considering the current performance and limitations of the available diagnostic tools for evaluation of cobalamin status and the presence of autoimmune chronic atrophic gastritis (CAG). Patients with PA require lifelong treatment with cobalamin replacement therapy. The current widely available treatment can be provided through enteral or parenteral cobalamin supplements, with comparable efficacy and tolerability.

A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

NASA Astrophysics Data System (ADS)

Lu, Feng; Wang, Yafan; Huang, Jinquan; Wang, Qihang

2016-09-01

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

Xpert®MTB/RIF for the Diagnosis of Tuberculosis in a Remote Arctic Setting: Impact on Cost and Time to Treatment Initiation.

PubMed

Oxlade, Olivia; Sugarman, Jordan; Alvarez, Gonzalo G; Pai, Madhukar; Schwartzman, Kevin

2016-01-01

Tuberculosis (TB) remains a significant health problem in the Canadian Arctic. Substantial health system delays in TB diagnosis can occur, in part due to the lack of capacity for onsite microbiologic testing. A study recently evaluated the yield and impact of a rapid automated PCR test (Xpert®MTB/RIF) for the diagnosis of TB in Iqaluit (Nunavut). We conducted an economic analysis to evaluate the expected cost relative to the expected reduction in time to treatment initiation, with the addition of Xpert®MTB/RIF to the current diagnostic and treatment algorithms used in this setting. A decision analysis model compared current microbiologic testing to a scenario where Xpert®MTB/RIF was added to the current diagnostic algorithm for active TB, and incorporated costs and clinical endpoints from the Iqaluit study. Several sensitivity analyses that considered alternative use were also considered. We estimated days to TB diagnosis and treatment initiation, health system costs, and the incremental cost per treatment day gained for each individual evaluated for possible TB. With the addition of Xpert®MTB/RIF, costs increased while days to TB treatment initiation were reduced. The incremental cost per treatment day gained (per individual investigated for TB) was $164 (95% uncertainty range $85, $452). In a sensitivity analysis that considered hospital discharge after a single negative Xpert®MTB/RIF, the Xpert®MTB/RIF scenario was cost saving. Adding Xpert®MTB/RIF to the current diagnostic algorithm for TB in Nunavut appears to reduce time to diagnosis and treatment at reasonable cost. It may be especially well suited to overcome some of the other logistical barriers that are unique to this and other remote communities.

Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting.

PubMed

Chan, Pak-Hei; Wong, Chun-Ka; Poh, Yukkee C; Pun, Louise; Leung, Wangie Wan-Chiu; Wong, Yu-Fai; Wong, Michelle Man-Ying; Poh, Ming-Zher; Chu, Daniel Wai-Sing; Siu, Chung-Wah

2016-07-21

Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera-based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real-world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single-lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77-99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51-87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97-99%] versus 99.4% [95% CI 99-100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38-67%] versus 76.9% [95% CI 56-91%]); both had a very high negative predictive value (99.8% [95% CI 99-100%] versus 99.2% [95% CI 98-100%]). The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population-based screening for AF. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Molecular Testing for miRNA, mRNA, and DNA on Fine-Needle Aspiration Improves the Preoperative Diagnosis of Thyroid Nodules With Indeterminate Cytology.

PubMed

Labourier, Emmanuel; Shifrin, Alexander; Busseniers, Anne E; Lupo, Mark A; Manganelli, Monique L; Andruss, Bernard; Wylie, Dennis; Beaudenon-Huibregtse, Sylvie

2015-07-01

Molecular testing for oncogenic mutations or gene expression in fine-needle aspirations (FNAs) from thyroid nodules with indeterminate cytology identifies a subset of benign or malignant lesions with high predictive value. This study aimed to evaluate a novel diagnostic algorithm combining mutation detection and miRNA expression to improve the diagnostic yield of molecular cytology. Surgical specimens and preoperative FNAs (n = 638) were tested for 17 validated gene alterations using the miRInform Thyroid test and with a 10-miRNA gene expression classifier generating positive (malignant) or negative (benign) results. Cross-sectional sampling of thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) or follicular neoplasm/suspicious for a follicular neoplasm (FN/SFN) cytology (n = 109) was conducted at 12 endocrinology centers across the United States. Qualitative molecular results were compared with surgical histopathology to determine diagnostic performance and model clinical effect. Mutations were detected in 69% of nodules with malignant outcome. Among mutation-negative specimens, miRNA testing correctly identified 64% of malignant cases and 98% of benign cases. The diagnostic sensitivity and specificity of the combined algorithm was 89% (95% confidence interval [CI], 73-97%) and 85% (95% CI, 75-92%), respectively. At 32% cancer prevalence, 61% of the molecular results were benign with a negative predictive value of 94% (95% CI, 85-98%). Independently of variations in cancer prevalence, the test increased the yield of true benign results by 65% relative to mRNA-based gene expression classification and decreased the rate of avoidable diagnostic surgeries by 69%. Multiplatform testing for DNA, mRNA, and miRNA can accurately classify benign and malignant thyroid nodules, increase the diagnostic yield of molecular cytology, and further improve the preoperative risk-based management of benign nodules with AUS/FLUS or FN/SFN cytology.

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

PubMed

Yoon, Yourim; Kim, Yong-Hyuk

2013-10-01

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

Study on text mining algorithm for ultrasound examination of chronic liver diseases based on spectral clustering

NASA Astrophysics Data System (ADS)

Chang, Bingguo; Chen, Xiaofei

2018-05-01

Ultrasonography is an important examination for the diagnosis of chronic liver disease. The doctor gives the liver indicators and suggests the patient's condition according to the description of ultrasound report. With the rapid increase in the amount of data of ultrasound report, the workload of professional physician to manually distinguish ultrasound results significantly increases. In this paper, we use the spectral clustering method to cluster analysis of the description of the ultrasound report, and automatically generate the ultrasonic diagnostic diagnosis by machine learning. 110 groups ultrasound examination report of chronic liver disease were selected as test samples in this experiment, and the results were validated by spectral clustering and compared with k-means clustering algorithm. The results show that the accuracy of spectral clustering is 92.73%, which is higher than that of k-means clustering algorithm, which provides a powerful ultrasound-assisted diagnosis for patients with chronic liver disease.

Evaluation of the Revised Algorithm of Autism Diagnostic Observation Schedule (ADOS) in the Diagnostic Investigation of High-Functioning Children and Adolescents with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Kamp-Becker, Inge; Ghahreman, Mardjan; Heinzel-Gutenbrunner, Monika; Peters, Mira; Remschmidt, Helmut; Becker, Katja

2013-01-01

The Autism Diagnostic Observation Schedule (ADOS) is a semi-structured, standardized assessment designed for use in diagnostic evaluation of individuals with suspected autism spectrum disorder (ASD). The ADOS has been effective in categorizing children who definitely have autism or not, but has lower specificity and sometimes sensitivity for…

Diagnostic tests and algorithms used in the investigation of haematuria: systematic reviews and economic evaluation.

PubMed

Rodgers, M; Nixon, J; Hempel, S; Aho, T; Kelly, J; Neal, D; Duffy, S; Ritchie, G; Kleijnen, J; Westwood, M

2006-06-01

To determine the most effective diagnostic strategy for the investigation of microscopic and macroscopic haematuria in adults. Electronic databases from inception to October 2003, updated in August 2004. A systematic review was undertaken according to published guidelines. Decision analytic modelling was undertaken, based on the findings of the review, expert opinion and additional information from the literature, to assess the relative cost-effectiveness of plausible alternative tests that are part of diagnostic algorithms for haematuria. A total of 118 studies met the inclusion criteria. No studies that evaluated the effectiveness of diagnostic algorithms for haematuria or the effectiveness of screening for haematuria or investigating its underlying cause were identified. Eighteen out of 19 identified studies evaluated dipstick tests and data from these suggested that these are moderately useful in establishing the presence of, but cannot be used to rule out, haematuria. Six studies using haematuria as a test for the presence of a disease indicated that the detection of microhaematuria cannot alone be considered a useful test either to rule in or rule out the presence of a significant underlying pathology (urinary calculi or bladder cancer). Forty-eight of 80 studies addressed methods to localise the source of bleeding (renal or lower urinary tract). The methods and thresholds described in these studies varied greatly, precluding any estimate of a 'best performance' threshold that could be applied across patient groups. However, studies of red blood cell morphology that used a cut-off value of 80% dysmorphic cells for glomerular disease reported consistently high specificities (potentially useful in ruling in a renal cause for haematuria). The reported sensitivities were generally low. Twenty-eight studies included data on the accuracy of laboratory tests (tumour markers, cytology) for the diagnosis of bladder cancer. The majority of tumour marker studies evaluated nuclear matrix protein 22 or bladder tumour antigen. The sensitivity and specificity ranges suggested that neither of these would be useful either for diagnosing bladder cancer or for ruling out patients for further investigation (cystoscopy). However, the evidence remains sparse and the diagnostic accuracy estimates varied widely between studies. Fifteen studies evaluating urine cytology as a test for urinary tract malignancies were heterogeneous and poorly reported. The calculated specificity values were generally high, suggesting some possible utility in confirming malignancy. However, the evidence suggests that urine cytology has no application in ruling out malignancy or excluding patients from further investigation. Fifteen studies evaluated imaging techniques [computed tomography (CT), intravenous urography (IVU) or ultrasound scanning (US)] to detect the underlying cause of haematuria. The target condition and the reference standard varied greatly between these studies. The diagnostic accuracy data for several individual studies appeared promising but meaningful comparison of the available imaging technologies was impossible. Eight studies met the inclusion criteria but addressed different parts of the diagnostic chain (e.g. screening programmes, laboratory investigations, full urological work-up). No single study addressed the complete diagnostic process. The review also highlighted a number of methodological limitations of these studies, including their lack of generalisability to the UK context. Separate decision analytic models were therefore developed to progress estimation of the optimal strategy for the diagnostic management of haematuria. The economic model for the detection of microhaematuria found that immediate microscopy following a positive dipstick test would improve diagnostic efficiency as it eliminates the high number of false positives produced by dipstick testing. Strategies that use routine microscopy may be associated with high numbers of false results, but evidence was lacking regarding the accuracy of routine microscopy and estimates were adopted for the model. The model for imaging the upper urinary tract showed that US detects more tumours than IVU at one-third of the cost, and is also associated with fewer false results. For any cause of haematuria, CT was shown to have a mean incremental cost-effectiveness ratio of pounds sterling 9939 in comparison with the next best option, US. When US is followed up with CT for negative results with persistent haematuria, it dominates the initial use of CT alone, with a saving of pounds sterling 235,000 for the evaluation of 1000 patients. The model for investigation of the lower urinary tract showed that for low-risk patients the use of immediate cystoscopy could be avoided if cystoscopy were used for follow-up patients with a negative initial test using tumour markers and/or cytology, resulting in a saving of pounds sterling 483,000 for the evaluation of 1000 patients. The clinical and economic impact on delayed detection of both upper and lower urinary tract tumours through the use of follow-up testing should be evaluated in future studies. There are insufficient data currently available to derive an evidence-based algorithm of the diagnostic pathway for haematuria. A hypothetical algorithm based on the opinion and practice of clinical experts in the review team, other published algorithms and the results of economic modelling is presented in this report. This algorithm is presented, for comparative purposes, alongside current US and UK guidelines. The ideas contained in these algorithms and the specific questions outlined should form the basis of future research. Quality assessment of the diagnostic accuracy studies included in this review highlighted several areas of deficiency.

Evaluation of a research diagnostic algorithm for DSM-5 neurocognitive disorders in a population-based cohort of older adults.

PubMed

Eramudugolla, Ranmalee; Mortby, Moyra E; Sachdev, Perminder; Meslin, Chantal; Kumar, Rajeev; Anstey, Kaarin J

2017-03-04

There is little information on the application and impact of revised criteria for diagnosing dementia and mild cognitive impairment (MCI), now termed major and mild neurocognitive disorders (NCDs) in the DSM-5. We evaluate a psychometric algorithm for diagnosing DSM-5 NCDs in a community-dwelling sample, and characterize the neuropsychological and functional profile of expert-diagnosed DSM-5 NCDs relative to DSM-IV dementia and International Working Group criteria for MCI. A population-based sample of 1644 adults aged 72-78 years was assessed. Algorithmic diagnostic criteria used detailed neuropsychological data, medical history, longitudinal cognitive performance, and informant interview. Those meeting all criteria for at least one diagnosis had data reviewed by a neurologist (expert diagnosis) who achieved consensus with a psychiatrist for complex cases. The algorithm accurately classified DSM-5 major NCD (area under the curve (AUC) = 0.95, 95% confidence interval (CI) 0.92-0.97), DSM-IV dementia (AUC = 0.91, 95% CI 0.85-0.97), DSM-5 mild NCD (AUC = 0.75, 95% CI 0.70-0.80), and MCI (AUC = 0.76, 95% CI 0.72-0.81) when compared to expert diagnosis. Expert diagnosis of dementia using DSM-5 criteria overlapped with 90% of DSM-IV dementia cases, but resulted in a 127% increase in diagnosis relative to DSM-IV. Additional cases had less severe memory, language impairment, and instrumental activities of daily living (IADL) impairments compared to cases meeting DSM-IV criteria for dementia. DSM-5 mild NCD overlapped with 83% of MCI cases and resulted in a 19% increase in diagnosis. These additional cases had a subtly different neurocognitive profile to MCI cases, including poorer social cognition. DSM-5 NCD criteria can be operationalized in a psychometric algorithm in a population setting. Expert diagnosis using DSM-5 NCD criteria captured most cases with DSM-IV dementia and MCI in our sample, but included many additional cases suggesting that DSM-5 criteria are broader in their categorization.

Interobserver Agreement for Contrast-Enhanced Ultrasound (CEUS)-Based Standardized Algorithms for the Diagnosis of Hepatocellular Carcinoma in High-Risk Patients.

PubMed

Schellhaas, Barbara; Pfeifer, Lukas; Kielisch, Christian; Goertz, Ruediger Stephan; Neurath, Markus F; Strobel, Deike

2018-06-07

 This pilot study aimed at assessing interobserver agreement with two contrast-enhanced ultrasound (CEUS) algorithms for the diagnosis of hepatocellular carcinoma (HCC) in high-risk patients.  Focal liver lesions in 55 high-risk patients were assessed independently by three blinded observers with two standardized CEUS algorithms: ESCULAP (Erlanger Synopsis of Contrast-Enhanced Ultrasound for Liver Lesion Assessment in Patients at risk) and ACR-CEUS-LI-RADSv.2016 (American College of Radiology CEUS-Liver Imaging Reporting and Data System). Lesions were categorized according to size and ultrasound contrast enhancement in the arterial, portal-venous and late phase. Interobserver agreement for assessment of enhancement pattern and categorization was compared between both CEUS algorithms. Additionally, diagnostic accuracy for the definitive diagnosis of HCC was compared. Histology and/or CE-MRI and follow-up served as reference standards.  55 patients were included in the study (male/female, 44/ 11; mean age: 65.9 years). 90.9 % had cirrhosis. Histological findings were available in 39/55 lesions (70.9 %). Reference standard of the 55 lesions revealed 48 HCCs, 2 intrahepatic cholangiocellular carcinomas (ICCs), and 5 non-HCC-non-ICC lesions. Interobserver agreement was moderate to substantial for arterial phase hyperenhancement (ĸ = 0.53 - 0.67), and fair to moderate for contrast washout in the portal-venous or late phase (ĸ = 0.33 - 0.53). Concerning the CEUS-based algorithms, the interreader agreement was substantial for the ESCULAP category (ĸ = 0.64 - 0.68) and fair for the CEUS-LI-RADS ® category (ĸ = 0.3 - 0.39). Disagreement between observers was mostly due to different perception of washout.  Interobserver agreement is better for ESCULAP than for CEUS-LI-RADS ® . This is mostly due to the fact that perception of contrast washout varies between different observers. However, interobserver agreement is good for arterial phase hyperenhancement, which is the key diagnostic feature for the diagnosis of HCC with CEUS in the cirrhotic liver. © Georg Thieme Verlag KG Stuttgart · New York.

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain.

PubMed

Hall, L O; Bensaid, A M; Clarke, L P; Velthuizen, R P; Silbiger, M S; Bezdek, J C

1992-01-01

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.

The sonographic features of malignant mediastinal lymph nodes and a proposal for an algorithmic approach for sampling during endobronchial ultrasound.

PubMed

Alici, Ibrahim Onur; Yılmaz Demirci, Nilgün; Yılmaz, Aydın; Karakaya, Jale; Özaydın, Esra

2016-09-01

There are several papers on the sonographic features of mediastinal lymph nodes affected by several diseases, but none gives the importance and clinical utility of the features. In order to find out which lymph node should be sampled in a particular nodal station during endobronchial ultrasound, we investigated the diagnostic performances of certain sonographic features and proposed an algorithmic approach. We retrospectively analyzed 1051 lymph nodes and randomly assigned them into a preliminary experimental and a secondary study group. The diagnostic performances of the sonographic features (gray scale, echogeneity, shape, size, margin, presence of necrosis, presence of calcification and absence of central hilar structure) were calculated, and an algorithm for lymph node sampling was obtained with decision tree analysis in the experimental group. Later, a modified algorithm was applied to the patients in the study group to give the accuracy. The demographic characteristics of the patients were not statistically significant between the primary and the secondary groups. All of the features were discriminative between malignant and benign diseases. The modified algorithm sensitivity, specificity, and positive and negative predictive values and diagnostic accuracy for detecting metastatic lymph nodes were 100%, 51.2%, 50.6%, 100% and 67.5%, respectively. In this retrospective analysis, the standardized sonographic classification system and the proposed algorithm performed well in choosing the node that should be sampled in a particular station during endobronchial ultrasound. © 2015 John Wiley & Sons Ltd.

Validation of administrative data used for the diagnosis of upper gastrointestinal events following nonsteroidal anti-inflammatory drug prescription.

PubMed

Abraham, N S; Cohen, D C; Rivers, B; Richardson, P

2006-07-15

To validate veterans affairs (VA) administrative data for the diagnosis of nonsteroidal anti-inflammatory drug (NSAID)-related upper gastrointestinal events (UGIE) and to develop a diagnostic algorithm. A retrospective study of veterans prescribed an NSAID as identified from the national pharmacy database merged with in-patient and out-patient data, followed by primary chart abstraction. Contingency tables were constructed to allow comparison with a random sample of patients prescribed an NSAID, but without UGIE. Multivariable logistic regression analysis was used to derive a predictive algorithm. Once derived, the algorithm was validated in a separate cohort of veterans. Of 906 patients, 606 had a diagnostic code for UGIE; 300 were a random subsample of 11 744 patients (control). Only 161 had a confirmed UGIE. The positive predictive value (PPV) of diagnostic codes was poor, but improved from 27% to 51% with the addition of endoscopic procedural codes. The strongest predictors of UGIE were an in-patient ICD-9 code for gastric ulcer, duodenal ulcer and haemorrhage combined with upper endoscopy. This algorithm had a PPV of 73% when limited to patients >or=65 years (c-statistic 0.79). Validation of the algorithm revealed a PPV of 80% among patients with an overlapping NSAID prescription. NSAID-related UGIE can be assessed using VA administrative data. The optimal algorithm includes an in-patient ICD-9 code for gastric or duodenal ulcer and gastrointestinal bleeding combined with a procedural code for upper endoscopy.

An Informative Interpretation of Decision Theory: The Information Theoretic Basis for Signal-to-Noise Ratio and Log Likelihood Ratio

DOE PAGES

Polcari, J.

2013-08-16

The signal processing concept of signal-to-noise ratio (SNR), in its role as a performance measure, is recast within the more general context of information theory, leading to a series of useful insights. Establishing generalized SNR (GSNR) as a rigorous information theoretic measure inherent in any set of observations significantly strengthens its quantitative performance pedigree while simultaneously providing a specific definition under general conditions. This directly leads to consideration of the log likelihood ratio (LLR): first, as the simplest possible information-preserving transformation (i.e., signal processing algorithm) and subsequently, as an absolute, comparable measure of information for any specific observation exemplar. Furthermore,more » the information accounting methodology that results permits practical use of both GSNR and LLR as diagnostic scalar performance measurements, directly comparable across alternative system/algorithm designs, applicable at any tap point within any processing string, in a form that is also comparable with the inherent performance bounds due to information conservation.« less

Improving staff response to seizures on the epilepsy monitoring unit with online EEG seizure detection algorithms.

PubMed

Rommens, Nicole; Geertsema, Evelien; Jansen Holleboom, Lisanne; Cox, Fieke; Visser, Gerhard

2018-05-11

User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time. EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU. EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h. EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures. Copyright © 2018. Published by Elsevier Inc.

The Collection 6 'dark-target' MODIS Aerosol Products

NASA Technical Reports Server (NTRS)

Levy, Robert C.; Mattoo, Shana; Munchak, Leigh A.; Kleidman, Richard G.; Patadia, Falguni; Gupta, Pawan; Remer, Lorraine

2013-01-01

Aerosol retrieval algorithms are applied to Moderate resolution Imaging Spectroradiometer (MODIS) sensors on both Terra and Aqua, creating two streams of decade-plus aerosol information. Products of aerosol optical depth (AOD) and aerosol size are used for many applications, but the primary concern is that these global products are comprehensive and consistent enough for use in climate studies. One of our major customers is the international modeling comparison study known as AEROCOM, which relies on the MODIS data as a benchmark. In order to keep up with the needs of AEROCOM and other MODIS data users, while utilizing new science and tools, we have improved the algorithms and products. The code, and the associated products, will be known as Collection 6 (C6). While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. In its entirety, the C6 algorithm is comprised of three sub-algorithms for retrieving aerosol properties over different surfaces: These include the dark-target DT algorithms to retrieve over (1) ocean and (2) vegetated-dark-soiled land, plus the (3) Deep Blue (DB) algorithm, originally developed to retrieve over desert-arid land. Focusing on the two DT algorithms, we have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to 84) to increase pole-ward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such as topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence in the retrieval, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time as we have introduced algorithm changes, we have also accounted for upstream changes including: new instrument calibration, revised land-sea masking, and changed cloud masking. Upstream changes also impact the coverage and global statistics of the retrieved AOD. Although our responsibility is to the DT code and products, we have also added a product that merges DT and DB product over semi-arid land surfaces to provide a more gap-free dataset, primarily for visualization purposes. Preliminary validation shows that compared to surface-based sunphotometer data, the C6, Level 2 (along swath) DT-products compare at least as well as those from C5. C6 will include new diagnostic information about clouds in the aerosol field, including an aerosol cloud mask at 500 m resolution, and calculations of the distance to the nearest cloud from clear pixels. Finally, we have revised the strategy for aggregating and averaging the Level 2 (swath) data to become Level 3 (gridded) data. All together, the changes to the DT algorithms will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. Changes in calibration will have more impact to Terras time series, especially over land. This will result in a significant reduction in artificial differences in the Terra and Aqua datasets, and will stabilize the MODIS data as a target for AEROCOM studie

Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms

NASA Astrophysics Data System (ADS)

Li, Shaoxin; Li, Linfang; Zeng, Qiuyao; Zhang, Yanjiao; Guo, Zhouyi; Liu, Zhiming; Jin, Mei; Su, Chengkang; Lin, Lin; Xu, Junfa; Liu, Songhao

2015-05-01

This study aims to characterize and classify serum surface-enhanced Raman spectroscopy (SERS) spectra between bladder cancer patients and normal volunteers by genetic algorithms (GAs) combined with linear discriminate analysis (LDA). Two group serum SERS spectra excited with nanoparticles are collected from healthy volunteers (n = 36) and bladder cancer patients (n = 55). Six diagnostic Raman bands in the regions of 481-486, 682-687, 1018-1034, 1313-1323, 1450-1459 and 1582-1587 cm-1 related to proteins, nucleic acids and lipids are picked out with the GAs and LDA. By the diagnostic models built with the identified six Raman bands, the improved diagnostic sensitivity of 90.9% and specificity of 100% were acquired for classifying bladder cancer patients from normal serum SERS spectra. The results are superior to the sensitivity of 74.6% and specificity of 97.2% obtained with principal component analysis by the same serum SERS spectra dataset. Receiver operating characteristic (ROC) curves further confirmed the efficiency of diagnostic algorithm based on GA-LDA technique. This exploratory work demonstrates that the serum SERS associated with GA-LDA technique has enormous potential to characterize and non-invasively detect bladder cancer through peripheral blood.

American Pancreatic Association Practice Guidelines in Chronic Pancreatitis: evidence-based report on diagnostic guidelines.

PubMed

Conwell, Darwin L; Lee, Linda S; Yadav, Dhiraj; Longnecker, Daniel S; Miller, Frank H; Mortele, Koenraad J; Levy, Michael J; Kwon, Richard; Lieb, John G; Stevens, Tyler; Toskes, Phillip P; Gardner, Timothy B; Gelrud, Andres; Wu, Bechien U; Forsmark, Christopher E; Vege, Santhi S

2014-11-01

The diagnosis of chronic pancreatitis remains challenging in early stages of the disease. This report defines the diagnostic criteria useful in the assessment of patients with suspected and established chronic pancreatitis. All current diagnostic procedures are reviewed, and evidence-based statements are provided about their utility and limitations. Diagnostic criteria for chronic pancreatitis are classified as definitive, probable, or insufficient evidence. A diagnostic (STEP-wise; survey, tomography, endoscopy, and pancreas function testing) algorithm is proposed that proceeds from a noninvasive to a more invasive approach. This algorithm maximizes specificity (low false-positive rate) in subjects with chronic abdominal pain and equivocal imaging changes. Furthermore, a nomenclature is suggested to further characterize patients with established chronic pancreatitis based on TIGAR-O (toxic, idiopathic, genetic, autoimmune, recurrent, and obstructive) etiology, gland morphology (Cambridge criteria), and physiologic state (exocrine, endocrine function) for uniformity across future multicenter research collaborations. This guideline will serve as a baseline manuscript that will be modified as new evidence becomes available and our knowledge of chronic pancreatitis improves.

Spectroscopic diagnosis of laryngeal carcinoma using near-infrared Raman spectroscopy and random recursive partitioning ensemble techniques.

PubMed

Teh, Seng Khoon; Zheng, Wei; Lau, David P; Huang, Zhiwei

2009-06-01

In this work, we evaluated the diagnostic ability of near-infrared (NIR) Raman spectroscopy associated with the ensemble recursive partitioning algorithm based on random forests for identifying cancer from normal tissue in the larynx. A rapid-acquisition NIR Raman system was utilized for tissue Raman measurements at 785 nm excitation, and 50 human laryngeal tissue specimens (20 normal; 30 malignant tumors) were used for NIR Raman studies. The random forests method was introduced to develop effective diagnostic algorithms for classification of Raman spectra of different laryngeal tissues. High-quality Raman spectra in the range of 800-1800 cm(-1) can be acquired from laryngeal tissue within 5 seconds. Raman spectra differed significantly between normal and malignant laryngeal tissues. Classification results obtained from the random forests algorithm on tissue Raman spectra yielded a diagnostic sensitivity of 88.0% and specificity of 91.4% for laryngeal malignancy identification. The random forests technique also provided variables importance that facilitates correlation of significant Raman spectral features with cancer transformation. This study shows that NIR Raman spectroscopy in conjunction with random forests algorithm has a great potential for the rapid diagnosis and detection of malignant tumors in the larynx.

Development of PET projection data correction algorithm

NASA Astrophysics Data System (ADS)

Bazhanov, P. V.; Kotina, E. D.

2017-12-01

Positron emission tomography is modern nuclear medicine method used in metabolism and internals functions examinations. This method allows to diagnosticate treatments on their early stages. Mathematical algorithms are widely used not only for images reconstruction but also for PET data correction. In this paper random coincidences and scatter correction algorithms implementation are considered, as well as algorithm of PET projection data acquisition modeling for corrections verification.

Development and improvement of the operating diagnostics systems of NPO CKTI works for turbine of thermal and nuclear power plants

NASA Astrophysics Data System (ADS)

Kovalev, I. A.; Rakovskii, V. G.; Isakov, N. Yu.; Sandovskii, A. V.

2016-03-01

The work results on the development and improvement of the techniques, algorithms, and software-hardware of continuous operating diagnostics systems of rotating units and parts of turbine equipment state are presented. In particular, to ensure the full remote service of monitored turbine equipment using web technologies, the web version of the software of the automated systems of vibration-based diagnostics (ASVD VIDAS) was developed. The experience in the automated analysis of data obtained by ASVD VIDAS form the basis of the new algorithm of early detection of such dangerous defects as rotor deflection, crack in the rotor, and strong misalignment of supports. The program-technical complex of monitoring and measuring the deflection of medium pressure rotor (PTC) realizing this algorithm will alert the electric power plant staff during a deflection and indicate its value. This will give the opportunity to take timely measures to prevent the further extension of the defect. Repeatedly, recorded cases of full or partial destruction of shrouded shelves of rotor blades of the last stages of low-pressure cylinders of steam turbines defined the need to develop a version of the automated system of blade diagnostics (ASBD SKALA) for shrouded stages. The processing, analysis, presentation, and backup of data characterizing the mechanical state of blade device are carried out with a newly developed controller of the diagnostics system. As a result of the implementation of the works, the diagnosed parameters determining the operation security of rotating elements of equipment was expanded and the new tasks on monitoring the state of units and parts of turbines were solved. All algorithmic solutions and hardware-software implementations mentioned in the article were tested on the test benches and applied at some power plants.

Assessing operating characteristics of CAD algorithms in the absence of a gold standard

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roy Choudhury, Kingshuk; Paik, David S.; Yi, Chin A.

2010-04-15

Purpose: The authors examine potential bias when using a reference reader panel as ''gold standard'' for estimating operating characteristics of CAD algorithms for detecting lesions. As an alternative, the authors propose latent class analysis (LCA), which does not require an external gold standard to evaluate diagnostic accuracy. Methods: A binomial model for multiple reader detections using different diagnostic protocols was constructed, assuming conditional independence of readings given true lesion status. Operating characteristics of all protocols were estimated by maximum likelihood LCA. Reader panel and LCA based estimates were compared using data simulated from the binomial model for a range ofmore » operating characteristics. LCA was applied to 36 thin section thoracic computed tomography data sets from the Lung Image Database Consortium (LIDC): Free search markings of four radiologists were compared to markings from four different CAD assisted radiologists. For real data, bootstrap-based resampling methods, which accommodate dependence in reader detections, are proposed to test of hypotheses of differences between detection protocols. Results: In simulation studies, reader panel based sensitivity estimates had an average relative bias (ARB) of -23% to -27%, significantly higher (p-value <0.0001) than LCA (ARB -2% to -6%). Specificity was well estimated by both reader panel (ARB -0.6% to -0.5%) and LCA (ARB 1.4%-0.5%). Among 1145 lesion candidates LIDC considered, LCA estimated sensitivity of reference readers (55%) was significantly lower (p-value 0.006) than CAD assisted readers' (68%). Average false positives per patient for reference readers (0.95) was not significantly lower (p-value 0.28) than CAD assisted readers' (1.27). Conclusions: Whereas a gold standard based on a consensus of readers may substantially bias sensitivity estimates, LCA may be a significantly more accurate and consistent means for evaluating diagnostic accuracy.« less

Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma.

PubMed

Schell, Greggory J; Lavieri, Mariel S; Helm, Jonathan E; Liu, Xiang; Musch, David C; Van Oyen, Mark P; Stein, Joshua D

2014-08-01

To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG). Secondary analyses using longitudinal data from 2 randomized controlled trials. A total of 571 participants from the Advanced Glaucoma Intervention Study (AGIS) and the Collaborative Initial Glaucoma Treatment Study (CIGTS). Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participant's disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participant's disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared with 1-, 1.5-, and 2-year fixed interval schedules of obtaining VF and IOP measurements. Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, and number of VF and IOP measurements needed to assess for progression. Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (P<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (P = 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well for patients with mild and advanced disease. The model performed significantly more testing of patients who exhibited OAG progression than nonprogressing patients (1.3 vs. 1.0 tests per year; P<0.0001). Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management. Copyright © 2014 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches.

PubMed

Çelik, Ufuk; Yurtay, Nilüfer; Koç, Emine Rabia; Tepe, Nermin; Güllüoğlu, Halil; Ertaş, Mustafa

2015-01-01

The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.

Development and clinical validation of a novel photography-based skin erythema evaluation system: a comparison with the calculated consensus of dermatologists.

PubMed

Cho, M; Lee, D-H; Doh, E J; Kim, Y; Chung, J H; Kim, H C; Kim, S

2017-08-01

Erythema is the most common presenting sign in patients with skin diseases, and various methods to treat erythema symptoms have become common. To evaluate changes in erythema, a reliable device that can support objective diagnosis is required. We developed a novel photography-based system for erythema diagnosis that provides a high-resolution three-view photograph taken in a consistent photography environment with a curved surface light source and can be integrated with optimized image processing algorithms. A new diagnostic algorithm was applied to photographs from 32 patients to determine areas of erythema automatically. To assess the performance in comparison to dermatologists' evaluations, five dermatologists independently evaluate the areas of erythema, and we defined an area called the clinical consensus area of erythema (CCAE), which is based on the majority opinion of dermatologists during evaluation. The CCAE values obtained were compared with the erythema areas determined by the system's diagnostic algorithm. Forty-one photographs with areas of erythema were evaluated by the proposed system and by dermatologists. The results obtained with the proposed system had a mean accuracy of 93.18% with a standard deviation of 3.52% when compared with the CCAE results. The results also showed that the proposed system could detect erythema areas without any pigmentation. In contrast to assessments by individual dermatologists, use of the CCAE reduced the amount of error that occurred owing to bias or subjectivity. A new erythema evaluation system was developed and validated through CCAE, suggesting that the system can support dermatologists' objective diagnoses of erythema. © 2017 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Fluorescence spectroscopy for diagnosis of squamous intraepithelial lesions of the cervix.

PubMed

Mitchell, M F; Cantor, S B; Ramanujam, N; Tortolero-Luna, G; Richards-Kortum, R

1999-03-01

To calculate receiver operating characteristic (ROC) curves for fluorescence spectroscopy in order to measure its performance in the diagnosis of squamous intraepithelial lesions (SILs) and to compare these curves with those for other diagnostic methods: colposcopy, cervicography, speculoscopy, Papanicolaou smear screening, and human papillomavirus (HPV) testing. Data from our previous clinical study were used to calculate ROC curves for fluorescence spectroscopy. Curves for other techniques were calculated from other investigators' reports. To identify these, a MEDLINE search for articles published from 1966 to 1996 was carried out, using the search terms "colposcopy," "cervicoscopy," "cervicography," "speculoscopy," "Papanicolaou smear," "HPV testing," "fluorescence spectroscopy," and "polar probe" in conjunction with the terms "diagnosis," "positive predictive value," "negative predictive value," and "receiver operating characteristic curve." We found 270 articles, from which articles were selected if they reported results of studies involving high-disease-prevalence populations, reported findings of studies in which colposcopically directed biopsy was the criterion standard, and included sufficient data for recalculation of the reported sensitivities and specificities. We calculated ROC curves for fluorescence spectroscopy using Bayesian and neural net algorithms. A meta-analytic approach was used to calculate ROC curves for the other techniques. Areas under the curves were calculated. Fluorescence spectroscopy using the neural net algorithm had the highest area under the ROC curve, followed by fluorescence spectroscopy using the Bayesian algorithm, followed by colposcopy, the standard diagnostic technique. Cervicography, Papanicolaou smear screening, and HPV testing performed comparably with each other but not as well as fluorescence spectroscopy and colposcopy. Fluorescence spectroscopy performs better than colposcopy and other techniques in the diagnosis of SILs. Because it also permits real-time diagnosis and has the potential of being used by inexperienced health care personnel, this technology holds bright promise.

A robust data scaling algorithm to improve classification accuracies in biomedical data.

PubMed

Cao, Xi Hang; Stojkovic, Ivan; Obradovic, Zoran

2016-09-09

Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy. To evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms. The proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.

Long-term surface EMG monitoring using K-means clustering and compressive sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza; Krishnan, Sridhar

2015-05-01

In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.

Pediatric chest HRCT using the iDose4 Hybrid Iterative Reconstruction Algorithm: Which iDose level to choose?

NASA Astrophysics Data System (ADS)

Smarda, M.; Alexopoulou, E.; Mazioti, A.; Kordolaimi, S.; Ploussi, A.; Priftis, K.; Efstathopoulos, E.

2015-09-01

Purpose of the study is to determine the appropriate iterative reconstruction (IR) algorithm level that combines image quality and diagnostic confidence, for pediatric patients undergoing high-resolution computed tomography (HRCT). During the last 2 years, a total number of 20 children up to 10 years old with a clinical presentation of chronic bronchitis underwent HRCT in our department's 64-detector row CT scanner using the iDose IR algorithm, with almost similar image settings (80kVp, 40-50 mAs). CT images were reconstructed with all iDose levels (level 1 to 7) as well as with filtered-back projection (FBP) algorithm. Subjective image quality was evaluated by 2 experienced radiologists in terms of image noise, sharpness, contrast and diagnostic acceptability using a 5-point scale (1=excellent image, 5=non-acceptable image). Artifacts existance was also pointed out. All mean scores from both radiologists corresponded to satisfactory image quality (score ≤3), even with the FBP algorithm use. Almost excellent (score <2) overall image quality was achieved with iDose levels 5 to 7, but oversmoothing artifacts appearing with iDose levels 6 and 7 affected the diagnostic confidence. In conclusion, the use of iDose level 5 enables almost excellent image quality without considerable artifacts affecting the diagnosis. Further evaluation is needed in order to draw more precise conclusions.

Macular ganglion cell imaging study: glaucoma diagnostic accuracy of spectral-domain optical coherence tomography.

PubMed

Jeoung, Jin Wook; Choi, Yun Jeong; Park, Ki Ho; Kim, Dong Myung

2013-07-01

We evaluated the diagnostic accuracy of macular ganglion cell-inner plexiform layer (GCIPL) measurements using a high-definition optical coherence tomography (Cirrus HD-OCT) ganglion cell analysis algorithm for detecting early and moderate-to-severe glaucoma. Totals of 119 normal subjects and 306 glaucoma patients (164 patients with early glaucoma and 142 with moderate-to-severe glaucoma) were enrolled from the Macular Ganglion Cell Imaging Study. Macular GCIPL, peripapillary retinal nerve fiber layer (RNFL) thickness, and optic nerve head (ONH) parameters were measured in each subject. Areas under the receiver operating characteristic curves (AUROCs) were calculated and compared. Based on the internal normative database, the sensitivity and specificity for detecting early and moderate-to-severe glaucoma were calculated. There was no statistically significant difference between the AUROCs for the best OCT parameters. For detecting early glaucoma, the sensitivity of the Cirrus GCIPL parameters ranged from 26.8% to 73.2% and that of the Cirrus RNFL parameters ranged from 6.1% to 61.6%. For the early glaucoma group, the best parameter from the GCIPL generally had a higher sensitivity than those of the RNFL and ONH parameters with comparable specificity (P < 0.05, McNemar's test). There were no significant differences between the AUROCs for Cirrus GCIPL, RNFL, and ONH parameters, indicating that these maps have similar diagnostic potentials for glaucoma. The minimum GCIPL showed better glaucoma diagnostic performance than the other parameters at comparable specificities. However, other GCIPL parameters showed performances comparable to those of the RNFL parameters.

Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

2008-03-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

Clinical diagnosis of ventilator associated pneumonia revisited: comparative validation using immediate post-mortem lung biopsies.

PubMed

Fàbregas, N; Ewig, S; Torres, A; El-Ebiary, M; Ramirez, J; de La Bellacasa, J P; Bauer, T; Cabello, H

1999-10-01

A study was undertaken to assess the diagnostic value of different clinical criteria and the impact of microbiological testing on the accuracy of clinical diagnosis of suspected ventilator associated pneumonia (VAP). Twenty five deceased mechanically ventilated patients were studied prospectively. Immediately after death, multiple bilateral lung biopsy specimens (16 specimens/patient) were obtained for histological examination and quantitative lung cultures. The presence of both histological pneumonia and positive lung cultures was used as a reference test. The presence of infiltrates on the chest radiograph and two of three clinical criteria (leucocytosis, purulent secretions, fever) had a sensitivity of 69% and a specificity of 75%; the corresponding numbers for the clinical pulmonary infection score (CPIS) were 77% and 42%. Non-invasive as well as invasive sampling techniques had comparable values. The combination of all techniques achieved a sensitivity of 85% and a specificity of 50%, and these values remained virtually unchanged despite the presence of previous treatment with antibiotics. When microbiological results were added to clinical criteria, adequate diagnoses originating from microbiological results which might have corrected false positive and false negative clinical judgements (n = 5) were countered by a similar proportion of inadequate diagnoses (n = 6). Clinical criteria had reasonable diagnostic values. CPIS was not superior to conventional clinical criteria. Non-invasive and invasive sampling techniques had diagnostic values comparable to clinical criteria. An algorithm guiding antibiotic treatment exclusively by microbiological results does not increase the overall diagnostic accuracy and carries the risk of undertreatment.

Evaluation of virtual monoenergetic imaging algorithms for dual-energy carotid and intracerebral CT angiography: Effects on image quality, artefacts and diagnostic performance for the detection of stenosis.

PubMed

Leithner, Doris; Mahmoudi, Scherwin; Wichmann, Julian L; Martin, Simon S; Lenga, Lukas; Albrecht, Moritz H; Booz, Christian; Arendt, Christophe T; Beeres, Martin; D'Angelo, Tommaso; Bodelle, Boris; Vogl, Thomas J; Scholtz, Jan-Erik

2018-02-01

To investigate the impact of traditional (VMI) and noise-optimized virtual monoenergetic imaging (VMI+) algorithms on quantitative and qualitative image quality, and the assessment of stenosis in carotid and intracranial dual-energy CTA (DE-CTA). DE-CTA studies of 40 patients performed on a third-generation 192-slice dual-source CT scanner were included in this retrospective study. 120-kVp image-equivalent linearly-blended, VMI and VMI+ series were reconstructed. Quantitative analysis included evaluation of contrast-to-noise ratios (CNR) of the aorta, common carotid artery, internal carotid artery, middle cerebral artery, and basilar artery. VMI and VMI+ with highest CNR, and linearly-blended series were rated qualitatively. Three radiologists assessed artefacts and suitability for evaluation at shoulder height, carotid bifurcation, siphon, and intracranial using 5-point Likert scales. Detection and grading of stenosis were performed at carotid bifurcation and siphon. Highest CNR values were observed for 40-keV VMI+ compared to 65-keV VMI and linearly-blended images (P < 0.001). Artefacts were low in all qualitatively assessed series with excellent suitability for supraaortic artery evaluation at shoulder and bifurcation height. Suitability was significantly higher in VMI+ and VMI compared to linearly-blended images for intracranial and ICA assessment (P < 0.002). VMI and VMI+ showed excellent accordance for detection and grading of stenosis at carotid bifurcation and siphon with no differences in diagnostic performance. 40-keV VMI+ showed improved quantitative image quality compared to 65-keV VMI and linearly-blended series in supraaortic DE-CTA. VMI and VMI+ provided increased suitability for carotid and intracranial artery evaluation with excellent assessment of stenosis, but did not translate into increased diagnostic performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Embedded Reasoning Supporting Aerospace IVHM

DTIC Science & Technology

2007-01-01

c method (BIT or health assessment algorithm) which the monitoring diagnostic relies on input information tics and Astronautics In the diagram...viewing of the current health state of all monitored subsystems, while also providing a means to probe deeper in the event anomalous operation is...seeks to integrate detection , diagnostic, and prognostic capabilities with a hierarchical diagnostic reasoning architecture into a single

An algorithm for improving the quality of structural images of turbid media in endoscopic optical coherence tomography

NASA Astrophysics Data System (ADS)

Potlov, A. Yu.; Frolov, S. V.; Proskurin, S. G.

2018-04-01

High-quality OCT structural images reconstruction algorithm for endoscopic optical coherence tomography of biological tissue is described. The key features of the presented algorithm are: (1) raster scanning and averaging of adjacent Ascans and pixels; (2) speckle level minimization. The described algorithm can be used in the gastroenterology, urology, gynecology, otorhinolaryngology for mucous membranes and skin diagnostics in vivo and in situ.

An algorithmic approach to the brain biopsy--part I.

PubMed

Kleinschmidt-DeMasters, B K; Prayson, Richard A

2006-11-01

The formulation of appropriate differential diagnoses for a slide is essential to the practice of surgical pathology but can be particularly challenging for residents and fellows. Algorithmic flow charts can help the less experienced pathologist to systematically consider all possible choices and eliminate incorrect diagnoses. They can assist pathologists-in-training in developing orderly, sequential, and logical thinking skills when confronting difficult cases. To present an algorithmic flow chart as an approach to formulating differential diagnoses for lesions seen in surgical neuropathology. An algorithmic flow chart to be used in teaching residents. Algorithms are not intended to be final diagnostic answers on any given case. Algorithms do not substitute for training received from experienced mentors nor do they substitute for comprehensive reading by trainees of reference textbooks. Algorithmic flow diagrams can, however, direct the viewer to the correct spot in reference texts for further in-depth reading once they hone down their diagnostic choices to a smaller number of entities. The best feature of algorithms is that they remind the user to consider all possibilities on each case, even if they can be quickly eliminated from further consideration. In Part I, we assist the resident in learning how to handle brain biopsies in general and how to distinguish nonneoplastic lesions that mimic tumors from true neoplasms.

An algorithmic approach to the brain biopsy--part II.

PubMed

Prayson, Richard A; Kleinschmidt-DeMasters, B K

2006-11-01

The formulation of appropriate differential diagnoses for a slide is essential to the practice of surgical pathology but can be particularly challenging for residents and fellows. Algorithmic flow charts can help the less experienced pathologist to systematically consider all possible choices and eliminate incorrect diagnoses. They can assist pathologists-in-training in developing orderly, sequential, and logical thinking skills when confronting difficult cases. To present an algorithmic flow chart as an approach to formulating differential diagnoses for lesions seen in surgical neuropathology. An algorithmic flow chart to be used in teaching residents. Algorithms are not intended to be final diagnostic answers on any given case. Algorithms do not substitute for training received from experienced mentors nor do they substitute for comprehensive reading by trainees of reference textbooks. Algorithmic flow diagrams can, however, direct the viewer to the correct spot in reference texts for further in-depth reading once they hone down their diagnostic choices to a smaller number of entities. The best feature of algorithms is that they remind the user to consider all possibilities on each case, even if they can be quickly eliminated from further consideration. In Part II, we assist the resident in arriving at the correct diagnosis for neuropathologic lesions containing granulomatous inflammation, macrophages, or abnormal blood vessels.

School-Based Screening for Suicide Risk: Balancing Costs and Benefits

PubMed Central

Wilcox, Holly; Huo, Yanling; Turner, J. Blake; Fisher, Prudence; Shaffer, David

2010-01-01

Objectives. We examined the effects of a scoring algorithm change on the burden and sensitivity of a screen for adolescent suicide risk. Methods. The Columbia Suicide Screen was used to screen 641 high school students for high suicide risk (recent ideation or lifetime attempt and depression, or anxiety, or substance use), determined by subsequent blind assessment with the Diagnostic Interview Schedule for Children. We compared the accuracy of different screen algorithms in identifying high-risk cases. Results. A screen algorithm comprising recent ideation or lifetime attempt or depression, anxiety, or substance-use problems set at moderate-severity level classed 35% of students as positive and identified 96% of high-risk students. Increasing the algorithm's threshold reduced the proportion identified to 24% and identified 92% of high-risk cases. Asking only about recent suicidal ideation or lifetime suicide attempt identified 17% of the students and 89% of high-risk cases. The proportion of nonsuicidal diagnosis–bearing students found with the 3 algorithms was 62%, 34%, and 12%, respectively. Conclusions. The Columbia Suicide Screen threshold can be altered to reduce the screen-positive population, saving costs and time while identifying almost all students at high risk for suicide. PMID:20634467

Evaluation of dried blood spot protocols with the Bio-Rad GS HIV Combo Ag/Ab EIA and Geenius™ HIV 1/2 Supplemental Assay.

PubMed

Luo, Wei; Davis, Geoff; Li, LiXia; Shriver, M Kathleen; Mei, Joanne; Styer, Linda M; Parker, Monica M; Smith, Amanda; Paz-Bailey, Gabriela; Ethridge, Steve; Wesolowski, Laura; Owen, S Michele; Masciotra, Silvina

2017-06-01

FDA-approved antigen/antibody combo and HIV-1/2 differentiation supplemental tests do not have claims for dried blood spot (DBS) use. We compared two DBS-modified protocols, the Bio-Rad GS HIV Combo Ag/Ab (BRC) EIA and Geenius™ HIV-1/2 (Geenius) Supplemental Assay, to plasma protocols and evaluated them in the CDC/APHL HIV diagnostic algorithm. BRC-DBS p24 analytical sensitivity was calculated from serial dilutions of p24. DBS specimens included 11 HIV-1 seroconverters, 151 HIV-1-positive individuals, including 20 on antiretroviral therapy, 31 HIV-2-positive and one HIV-1/HIV-2-positive individuals. BRC-reactive specimens were tested with Geenius using the same DBS eluate. Matched plasma specimens were tested with BRC, an IgG/IgM immunoassay and Geenius. DBS and plasma results were compared using the McNemar's test. A DBS-algorithm applied to 348 DBS from high-risk individuals who participated in surveillance was compared to HIV status based on local testing algorithms. BRC-DBS detects p24 at a concentration 18 times higher than in plasma. In seroconverters, BRC-DBS detected more infections than the IgG/IgM immunoassay in plasma (p=0.0133), but fewer infections than BRC-plasma (p=0.0133). In addition, the BRC/Geenius-plasma algorithm identified more HIV-1 infections than the BRC/Geenius-DBS algorithm (p=0.0455). The DBS protocols correctly identified HIV status for established HIV-1 infections, including those on therapy, HIV-2 infections, and surveillance specimens. The DBS protocols exhibited promising performance and allowed rapid supplemental testing. Although the DBS algorithm missed some early infections, it showed similar results when applied to specimens from a high-risk population. Implementation of a DBS algorithm would benefit testing programs without capacity for venipuncture. Published by Elsevier B.V.

Deep learning based syndrome diagnosis of chronic gastritis.

PubMed

Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

2014-01-01

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

Open Energy Information System version 2.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

OpenEIS was created to provide standard methods for authoring, sharing, testing, using, and improving algorithms for operational building energy efficiency with building managers and building owners. OpenEIS is designed as a no-cost/low-cost solution that will propagate the fault detection and diagnostic (FDD) solutions into the marketplace by providing state- of- the-art analytical and diagnostic algorithms. As OpenEIS penetrates the market, demand by control system manufacturers and integrators serving small and medium commercial customers will help push these types of commercial software tool offerings into the broader marketplace.

Deep Learning Based Syndrome Diagnosis of Chronic Gastritis

PubMed Central

Liu, Guo-Ping; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

2014-01-01

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:24734118

Indications for MARS-MRI in Patients Treated With Articular Surface Replacement XL Total Hip Arthroplasty.

PubMed

Connelly, James W; Galea, Vincent P; Laaksonen, Inari; Matuszak, Sean J; Madanat, Rami; Muratoglu, Orhun; Malchau, Henrik

2018-04-19

The purpose of this study was to identify which patient and clinical factors are predictive of adverse local tissue reaction (ALTR) and to use these factors to create a highly sensitive algorithm for indicating metal artifact reduction sequence magnetic resonance imaging (MARS-MRI) in Articular Surface Replacement (ASR) XL total hip arthroplasty patients. Our secondary aim was to compare our algorithm to existing national guidelines on when to take MARS-MRI in metal-on-metal total hip arthroplasty patients. The study consisted of 137 patients treated with unilateral ASR XL implants from a prospective, multicenter study. Patients underwent MARS-MRI regardless of clinical presentation at a mean of 6.2 (range, 3.3-10.4) years from surgery. Univariate and multivariate analyses were conducted to determine which variables were predictive of ALTR. Predictors were used to create an algorithm to indicate MARS-MRI. Finally, we compared our algorithm's ability to detect ALTR to existing guidelines. We found a visual analog scale pain score ≥2 (odds ratio [OR] = 2.53; P = .023), high blood cobalt (OR = 1.05; P = .023), and male gender (OR = 2.37; P = .034) to be significant predictors of ALTR presence in our cohort. The resultant algorithm achieved 86.4% sensitivity and 60.2% specificity in detecting ALTR within our cohort. Our algorithm had the highest area under the curve and was the only guideline that was significantly predictive of ALTR (P = .014). Our algorithm including patient-reported pain and sex-specific cutoffs for blood cobalt levels could predict ALTR and indicate MARS-MRI in our cohort of ASR XL metal-on-metal patients with high sensitivity. Level II, diagnostic study. Copyright © 2018 Elsevier Inc. All rights reserved.

Automatic detection and classification of artifacts in single-channel EEG.

PubMed

Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W; Sorensen, Helge B D

2014-01-01

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.

Implementation of several mathematical algorithms to breast tissue density classification

NASA Astrophysics Data System (ADS)

Quintana, C.; Redondo, M.; Tirao, G.

2014-02-01

The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories.

Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.

PubMed

Matsuba, Shinji; Tabuchi, Hitoshi; Ohsugi, Hideharu; Enno, Hiroki; Ishitobi, Naofumi; Masumoto, Hiroki; Kiuchi, Yoshiaki

2018-05-09

To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an ultra-wide-field fundus imaging system. First, to evaluate the diagnostic accuracy of DCNN, 364 photographic images (AMD: 137) were amplified and the area under the curve (AUC), sensitivity and specificity were examined. Furthermore, in order to compare the diagnostic abilities between DCNN and six ophthalmologists, we prepared yield 84 sheets comprising 50% of normal and wet-AMD data each, and calculated the correct answer rate, specificity, sensitivity, and response times. DCNN exhibited 100% sensitivity and 97.31% specificity for wet-AMD images, with an average AUC of 99.76%. Moreover, comparing the diagnostic abilities of DCNN versus six ophthalmologists, the average accuracy of the DCNN was 100%. On the other hand, the accuracy of ophthalmologists, determined only by Optos images without a fundus examination, was 81.9%. A combination of DCNN with Optos images is not better than a medical examination; however, it can identify exudative AMD with a high level of accuracy. Our system is considered useful for screening and telemedicine.

Algorithms in the First-Line Treatment of Metastatic Clear Cell Renal Cell Carcinoma--Analysis Using Diagnostic Nodes.

PubMed

Rothermundt, Christian; Bailey, Alexandra; Cerbone, Linda; Eisen, Tim; Escudier, Bernard; Gillessen, Silke; Grünwald, Viktor; Larkin, James; McDermott, David; Oldenburg, Jan; Porta, Camillo; Rini, Brian; Schmidinger, Manuela; Sternberg, Cora; Putora, Paul M

2015-09-01

With the advent of targeted therapies, many treatment options in the first-line setting of metastatic clear cell renal cell carcinoma (mccRCC) have emerged. Guidelines and randomized trial reports usually do not elucidate the decision criteria for the different treatment options. In order to extract the decision criteria for the optimal therapy for patients, we performed an analysis of treatment algorithms from experts in the field. Treatment algorithms for the treatment of mccRCC from experts of 11 institutions were obtained, and decision trees were deduced. Treatment options were identified and a list of unified decision criteria determined. The final decision trees were analyzed with a methodology based on diagnostic nodes, which allows for an automated cross-comparison of decision trees. The most common treatment recommendations were determined, and areas of discordance were identified. The analysis revealed heterogeneity in most clinical scenarios. The recommendations selected for first-line treatment of mccRCC included sunitinib, pazopanib, temsirolimus, interferon-α combined with bevacizumab, high-dose interleukin-2, sorafenib, axitinib, everolimus, and best supportive care. The criteria relevant for treatment decisions were performance status, Memorial Sloan Kettering Cancer Center risk group, only or mainly lung metastases, cardiac insufficiency, hepatic insufficiency, age, and "zugzwang" (composite of multiple, related criteria). In the present study, we used diagnostic nodes to compare treatment algorithms in the first-line treatment of mccRCC. The results illustrate the heterogeneity of the decision criteria and treatment strategies for mccRCC and how available data are interpreted and implemented differently among experts. The data provided in the present report should not be considered to serve as treatment recommendations for the management of treatment-naïve patients with multiple metastases from metastatic clear cell renal cell carcinoma outside a clinical trial; however, the data highlight the different treatment options and the criteria used to select them. The diversity in decision making and how results from phase III trials can be interpreted and implemented differently in daily practice are demonstrated. ©AlphaMed Press.

A Testbed for Data Fusion for Helicopter Diagnostics and Prognostics

DTIC Science & Technology

2003-03-01

and algorithm design and tuning in order to develop advanced diagnostic and prognostic techniques for air craft health monitoring . Here a...and development of models for diagnostics, prognostics , and anomaly detection . Figure 5 VMEP Server Browser Interface 7 Download... detections , and prognostic prediction time horizons. The VMEP system and in particular the web component are ideal for performing data collection

Using Standardized Diagnostic Instruments to Classify Children with Autism in the Study to Explore Early Development

ERIC Educational Resources Information Center

Wiggins, Lisa D.; Reynolds, Ann; Rice, Catherine E.; Moody, Eric J.; Bernal, Pilar; Blaskey, Lisa; Rosenberg, Steven A.; Lee, Li-Ching; Levy, Susan E.

2015-01-01

The Study to Explore Early Development (SEED) is a multi-site case-control study designed to explore the relationship between autism spectrum disorder (ASD) phenotypes and etiologies. The goals of this paper are to (1) describe the SEED algorithm that uses the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule…

Multisite Study of New Autism Diagnostic Interview-Revised (ADI-R) Algorithms for Toddlers and Young Preschoolers

ERIC Educational Resources Information Center

Kim, So Hyun; Thurm, Audrey; Shumway, Stacy; Lord, Catherine

2013-01-01

Using two independent datasets provided by National Institute of Health funded consortia, the Collaborative Programs for Excellence in Autism and Studies to Advance Autism Research and Treatment (n = 641) and the National Institute of Mental Health (n = 167), diagnostic validity and factor structure of the new Autism Diagnostic Interview (ADI-R)…

Multivariate Analysis As a Support for Diagnostic Flowcharts in Allergic Bronchopulmonary Aspergillosis: A Proof-of-Concept Study.

PubMed

Vitte, Joana; Ranque, Stéphane; Carsin, Ania; Gomez, Carine; Romain, Thomas; Cassagne, Carole; Gouitaa, Marion; Baravalle-Einaudi, Mélisande; Bel, Nathalie Stremler-Le; Reynaud-Gaubert, Martine; Dubus, Jean-Christophe; Mège, Jean-Louis; Gaudart, Jean

2017-01-01

Molecular-based allergy diagnosis yields multiple biomarker datasets. The classical diagnostic score for allergic bronchopulmonary aspergillosis (ABPA), a severe disease usually occurring in asthmatic patients and people with cystic fibrosis, comprises succinct immunological criteria formulated in 1977: total IgE, anti- Aspergillus fumigatus ( Af ) IgE, anti- Af "precipitins," and anti- Af IgG. Progress achieved over the last four decades led to multiple IgE and IgG(4) Af biomarkers available with quantitative, standardized, molecular-level reports. These newly available biomarkers have not been included in the current diagnostic criteria, either individually or in algorithms, despite persistent underdiagnosis of ABPA. Large numbers of individual biomarkers may hinder their use in clinical practice. Conversely, multivariate analysis using new tools may bring about a better chance of less diagnostic mistakes. We report here a proof-of-concept work consisting of a three-step multivariate analysis of Af IgE, IgG, and IgG4 biomarkers through a combination of principal component analysis, hierarchical ascendant classification, and classification and regression tree multivariate analysis. The resulting diagnostic algorithms might show the way for novel criteria and improved diagnostic efficiency in Af -sensitized patients at risk for ABPA.

A new computerized diagnostic algorithm for quantitative evaluation of binocular misalignment in patients with strabismus

NASA Astrophysics Data System (ADS)

Nam, Kyoung Won; Kim, In Young; Kang, Ho Chul; Yang, Hee Kyung; Yoon, Chang Ki; Hwang, Jeong Min; Kim, Young Jae; Kim, Tae Yun; Kim, Kwang Gi

2012-10-01

Accurate measurement of binocular misalignment between both eyes is important for proper preoperative management, surgical planning, and postoperative evaluation of patients with strabismus. In this study, we proposed a new computerized diagnostic algorithm that can calculate the angle of binocular eye misalignment photographically by using a dedicated three-dimensional eye model mimicking the structure of the natural human eye. To evaluate the performance of the proposed algorithm, eight healthy volunteers and eight individuals with strabismus were recruited in this study, the horizontal deviation angle, vertical deviation angle, and angle of eye misalignment were calculated and the angular differences between the healthy and the strabismus groups were evaluated using the nonparametric Mann-Whitney test and the Pearson correlation test. The experimental results demonstrated a statistically significant difference between the healthy and strabismus groups (p = 0.015 < 0.05), but no statistically significant difference between the proposed method and the Krimsky test (p = 0.912 > 0.05). The measurements of the two methods were highly correlated (r = 0.969, p < 0.05). From the experimental results, we believe that the proposed diagnostic method has the potential to be a diagnostic tool that measures the physical disorder of the human eye to diagnose non-invasively the severity of strabismus.

Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tourassi, Georgia; Voisin, Sophie; Paquit, Vincent C

2013-01-01

Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By poolingmore » the data from all radiologists machine learning produced highly accurate predictive models linking image content, gaze, cognition, and error. Merging radiologists gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the radiologists diagnostic errors while confirming 96.2% of their correct diagnoses. The radiologists individual errors could be adequately predicted by modeling the behavior of their peers. However, personalized tuning appears to be beneficial in many cases to capture more accurately individual behavior. Conclusions: Machine learning algorithms combining image features with radiologists gaze data and diagnostic decisions can be effectively developed to recognize cognitive and perceptual errors associated with the diagnostic interpretation of mammograms.« less

Modeling paradigms for medical diagnostic decision support: a survey and future directions.

PubMed

Wagholikar, Kavishwar B; Sundararajan, Vijayraghavan; Deshpande, Ashok W

2012-10-01

Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that-(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians' utilization of these decision aids.

On-line experimental validation of a model-based diagnostic algorithm dedicated to a solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Polverino, Pierpaolo; Esposito, Angelo; Pianese, Cesare; Ludwig, Bastian; Iwanschitz, Boris; Mai, Andreas

2016-02-01

In the current energetic scenario, Solid Oxide Fuel Cells (SOFCs) exhibit appealing features which make them suitable for environmental-friendly power production, especially for stationary applications. An example is represented by micro-combined heat and power (μ-CHP) generation units based on SOFC stacks, which are able to produce electric and thermal power with high efficiency and low pollutant and greenhouse gases emissions. However, the main limitations to their diffusion into the mass market consist in high maintenance and production costs and short lifetime. To improve these aspects, the current research activity focuses on the development of robust and generalizable diagnostic techniques, aimed at detecting and isolating faults within the entire system (i.e. SOFC stack and balance of plant). Coupled with appropriate recovery strategies, diagnosis can prevent undesired system shutdowns during faulty conditions, with consequent lifetime increase and maintenance costs reduction. This paper deals with the on-line experimental validation of a model-based diagnostic algorithm applied to a pre-commercial SOFC system. The proposed algorithm exploits a Fault Signature Matrix based on a Fault Tree Analysis and improved through fault simulations. The algorithm is characterized on the considered system and it is validated by means of experimental induction of faulty states in controlled conditions.

Efficient fault diagnosis of helicopter gearboxes

NASA Technical Reports Server (NTRS)

Chin, H.; Danai, K.; Lewicki, D. G.

1993-01-01

Application of a diagnostic system to a helicopter gearbox is presented. The diagnostic system is a nonparametric pattern classifier that uses a multi-valued influence matrix (MVIM) as its diagnostic model and benefits from a fast learning algorithm that enables it to estimate its diagnostic model from a small number of measurement-fault data. To test this diagnostic system, vibration measurements were collected from a helicopter gearbox test stand during accelerated fatigue tests and at various fault instances. The diagnostic results indicate that the MVIM system can accurately detect and diagnose various gearbox faults so long as they are included in training.

Automated identification of patients with pulmonary nodules in an integrated health system using administrative health plan data, radiology reports, and natural language processing.

PubMed

Danforth, Kim N; Early, Megan I; Ngan, Sharon; Kosco, Anne E; Zheng, Chengyi; Gould, Michael K

2012-08-01

Lung nodules are commonly encountered in clinical practice, yet little is known about their management in community settings. An automated method for identifying patients with lung nodules would greatly facilitate research in this area. Using members of a large, community-based health plan from 2006 to 2010, we developed a method to identify patients with lung nodules, by combining five diagnostic codes, four procedural codes, and a natural language processing algorithm that performed free text searches of radiology transcripts. An experienced pulmonologist reviewed a random sample of 116 radiology transcripts, providing a reference standard for the natural language processing algorithm. With the use of an automated method, we identified 7112 unique members as having one or more incident lung nodules. The mean age of the patients was 65 years (standard deviation 14 years). There were slightly more women (54%) than men, and Hispanics and non-whites comprised 45% of the lung nodule cohort. Thirty-six percent were never smokers whereas 11% were current smokers. Fourteen percent of the patients were subsequently diagnosed with lung cancer. The sensitivity and specificity of the natural language processing algorithm for identifying the presence of lung nodules were 96% and 86%, respectively, compared with clinician review. Among the true positive transcripts in the validation sample, only 35% were solitary and unaccompanied by one or more associated findings, and 56% measured 8 to 30 mm in diameter. A combination of diagnostic codes, procedural codes, and a natural language processing algorithm for free text searching of radiology reports can accurately and efficiently identify patients with incident lung nodules, many of whom are subsequently diagnosed with lung cancer.

Validation of existing diagnosis of autism in mainland China using standardised diagnostic instruments.

PubMed

Sun, Xiang; Allison, Carrie; Auyeung, Bonnie; Zhang, Zhixiang; Matthews, Fiona E; Baron-Cohen, Simon; Brayne, Carol

2015-11-01

Research to date in mainland China has mainly focused on children with autistic disorder rather than Autism Spectrum Conditions and the diagnosis largely depended on clinical judgment without the use of diagnostic instruments. Whether children who have been diagnosed in China before meet the diagnostic criteria of Autism Spectrum Conditions is not known nor how many such children would meet these criteria. The aim of this study was to evaluate children with a known diagnosis of autism in mainland China using the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview-Revised to verify that children who were given a diagnosis of autism made by Chinese clinicians in China were mostly children with severe autism. Of 50 children with an existing diagnosis of autism made by Chinese clinicians, 47 children met the diagnosis of autism on the Autism Diagnostic Observation Schedule algorithm and 44 children met the diagnosis of autism on the Autism Diagnostic Interview-Revised algorithm. Using the Gwet's alternative chance-corrected statistic, the agreement between the Chinese diagnosis and the Autism Diagnostic Observation Schedule diagnosis was very good (AC1 = 0.94, p < 0.005, 95% confidence interval (0.86, 1.00)), so was the agreement between the Chinese diagnosis and the Autism Diagnostic Interview-Revised (AC1 = 0.91, p < 0.005, 95% confidence interval (0.81, 1.00)). The agreement between the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview-Revised was lower but still very good (AC1 = 0.83, p < 0.005). © The Author(s) 2015.

Validation of existing diagnosis of autism in mainland China using standardised diagnostic instruments

PubMed Central

Sun, Xiang; Allison, Carrie; Auyeung, Bonnie; Zhang, Zhixiang; Matthews, Fiona E; Baron-Cohen, Simon; Brayne, Carol

2016-01-01

Research to date in mainland China has mainly focused on children with autistic disorder rather than Autism Spectrum Conditions and the diagnosis largely depended on clinical judgment without the use of diagnostic instruments. Whether children who have been diagnosed in China before meet the diagnostic criteria of Autism Spectrum Conditions is not known nor how many such children would meet these criteria. The aim of this study was to evaluate children with a known diagnosis of autism in mainland China using the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview–Revised to verify that children who were given a diagnosis of autism made by Chinese clinicians in China were mostly children with severe autism. Of 50 children with an existing diagnosis of autism made by Chinese clinicians, 47 children met the diagnosis of autism on the Autism Diagnostic Observation Schedule algorithm and 44 children met the diagnosis of autism on the Autism Diagnostic Interview–Revised algorithm. Using the Gwet’s alternative chance-corrected statistic, the agreement between the Chinese diagnosis and the Autism Diagnostic Observation Schedule diagnosis was very good (AC1 = 0.94, p < 0.005, 95% confidence interval (0.86, 1.00)), so was the agreement between the Chinese diagnosis and the Autism Diagnostic Interview–Revised (AC1 = 0.91, p < 0.005, 95% confidence interval (0.81, 1.00)). The agreement between the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview–Revised was lower but still very good (AC1 = 0.83, p < 0.005). PMID:25757721

American Pancreatic Association Practice Guidelines in Chronic Pancreatitis: Evidence-Based Report on Diagnostic Guidelines

PubMed Central

Conwell, Darwin L.; Lee, Linda S.; Yadav, Dhiraj; Longnecker, Daniel S.; Miller, Frank H.; Mortele, Koenraad J.; Levy, Michael J.; Kwon, Richard; Lieb, John G.; Stevens, Tyler; Toskes, Philip P.; Gardner, Timothy B.; Gelrud, Andres; Wu, Bechien U.; Forsmark, Christopher E.; Vege, Santhi S.

2016-01-01

The diagnosis of chronic pancreatitis remains challenging in early stages of the disease. This report defines the diagnostic criteria useful in the assessment of patients with suspected and established chronic pancreatitis. All current diagnostic procedures are reviewed and evidence based statements are provided about their utility and limitations. Diagnostic criteria for chronic pancreatitis are classified as definitive, probable or insufficient evidence. A diagnostic (STEP-wise; S-survey, T-tomography, E-endoscopy and P-pancreas function testing) algorithm is proposed that proceeds from a non-invasive to a more invasive approach. This algorithm maximizes specificity (low false positive rate) in subjects with chronic abdominal pain and equivocal imaging changes. Futhermore, a nomenclature is suggested to further characterize patients with established chronic pancreatitis based on TIGAR-O (T-toxic, I-idiopathic, G-genetic, A- autoimmune, R-recurrent and O-obstructive) etiology, gland morphology (Cambridge criteria) and physiologic state (exocrine, endocrine function) for uniformity across future multi-center research collaborations. This guideline will serve as a baseline manuscript that will be modified as new evidence becomes available and our knowledge of chronic pancreatitis improves. PMID:25333398

Decentralized diagnostics based on a distributed micro-genetic algorithm for transducer networks monitoring large experimental systems.

PubMed

Arpaia, P; Cimmino, P; Girone, M; La Commara, G; Maisto, D; Manna, C; Pezzetti, M

2014-09-01

Evolutionary approach to centralized multiple-faults diagnostics is extended to distributed transducer networks monitoring large experimental systems. Given a set of anomalies detected by the transducers, each instance of the multiple-fault problem is formulated as several parallel communicating sub-tasks running on different transducers, and thus solved one-by-one on spatially separated parallel processes. A micro-genetic algorithm merges evaluation time efficiency, arising from a small-size population distributed on parallel-synchronized processors, with the effectiveness of centralized evolutionary techniques due to optimal mix of exploitation and exploration. In this way, holistic view and effectiveness advantages of evolutionary global diagnostics are combined with reliability and efficiency benefits of distributed parallel architectures. The proposed approach was validated both (i) by simulation at CERN, on a case study of a cold box for enhancing the cryogeny diagnostics of the Large Hadron Collider, and (ii) by experiments, under the framework of the industrial research project MONDIEVOB (Building Remote Monitoring and Evolutionary Diagnostics), co-funded by EU and the company Del Bo srl, Napoli, Italy.

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images.

PubMed

Møllersen, Kajsa; Zortea, Maciel; Schopf, Thomas R; Kirchesch, Herbert; Godtliebsen, Fred

2017-01-01

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

3D-CAM: Derivation and Validation of a 3-Minute Diagnostic Interview for CAM-defined Delirium

PubMed Central

Marcantonio, Edward R.; Ngo, Long H.; O’Connor, Margaret; Jones, Richard N.; Crane, Paul K.; Metzger, Eran D.; Inouye, Sharon K.

2015-01-01

Background Delirium is common, morbid, and costly, yet remains often unrecognized in most clinical settings. The Confusion Assessment Method (CAM) is the most widely used diagnostic algorithm, and operationalizing its features would represent a substantial advance for clinical care. Objective To derive the 3D-CAM, a new 3-minute diagnostic assessment for CAM-defined delirium, and to validate it against a clinical reference standard. Design Diagnostic test study Setting 4 general medicine units in an academic medical center Participants 201 inpatients aged ≥ 75 years old Measurements We identified 20 items that best operationalized the 4 CAM diagnostic features to create the 3D-CAM. For prospective validation, 3D-CAM assessments were administered by trained research assistants. Independently, clinicians performed an extensive assessment that included patient interviews, family interviews, and review of the medical record. These data were considered by an expert panel to determine the presence or absence of delirium and dementia (reference standard). We compared the 3D-CAM delirium determination to the reference standard in all patients and in subgroups with and without dementia. Results The 201 participants in the prospective validation study had mean age (SD) of 84 (5.5) years, and 27% had dementia. The expert panel identified delirium in 21%. Median administration time for 3D-CAM was 3 minutes (inter-quartile range: 2–5 minutes). The sensitivity [95% CI] of 3D-CAM was 95% [84%, 99%] and the specificity was 94% [90%, 97%]. The 3D-CAM performed well in patients both with dementia (sensitivity=96% [82%, 100%], specificity=86% [67%, 96%]) and without dementia (sensitivity=93% [66%, 100%], specificity=96% [91%,99%]). Limitations Limited to single center, cross-sectional, and medicine patients only Conclusion The 3D-CAM operationalizes the CAM algorithm using a 3-minute structured assessment with high sensitivity and specificity relative to a reference standard and could be an important tool for improving recognition of delirium. PMID:25329203

[The preoperative thoracic X-ray for tactical decisions for the thoracic injuries treatment].

PubMed

Voskresenskiĭ, O V; Beresneva, É A; Sharifullin, F A; Popova, I E; Abakumov, M M

2011-01-01

Data of 379 patients with penetrating thoracic wounds were analyzed. The pathologic changes on X-ray of the thoracic cavity were registered 239 (63,1%) patients: the hemothorax was diagnosed in 44,3%, pneumothorax - in 26,8% and hemopneumothorax - in 28,9%. 154 patients had videothoracoscopic surgery and 225 patients were operated on using traditional open methods. Operative findings were compared with X-ray data. The sensitivity of plain chest radiography in diagnostics of hemothorax was 52,1%, the specificity - 92,1%. Mistakes of interpreting X-ray data in diagnosing of low-volume hemo- or pneumothorax were defined. The computed tomography of the thorax proved to be the most precise means of intrapleural injuries diagnostics. The optimal algorithm of preoperative thoracic X-ray was suggested.

Differentiating between autism spectrum disorders and other developmental disabilities in children who failed a screening instrument for ASD.

PubMed

Ventola, Pamela; Kleinman, Jamie; Pandey, Juhi; Wilson, Leandra; Esser, Emma; Boorstein, Hilary; Dumont-Mathieu, Thyde; Marshia, Gail; Barton, Marianne; Hodgson, Sarah; Green, James; Volkmar, Fred; Chawarska, Katarzyna; Babitz, Tammy; Robins, Diana; Fein, Deborah

2007-03-01

This study compared behavioral presentation of toddlers with autistic spectrum disorders (ASD) and toddlers with global developmental delay (DD) or developmental language disorder (DLD) who display some characteristics of ASD using the diagnostic algorithm items from the Autism Diagnostic Observation Schedule, Generic (ADOS), the Childhood Autism Rating Scale (CARS), and Modified Checklist for Autism in Toddlers (M-CHAT). To date, 195 children have failed the M-CHAT and have been diagnosed with ASD, DD or DLD. Children with ASD had prominent and consistent impairments in socialization skills, especially joint attention skills and were more impaired in some aspects of communication, play, and sensory processing. Children with ASD and children with DD/DLD shared common features, but certain behavioral markers differentiated the two groups.

Periprosthetic joint infections: a clinical practice algorithm.

PubMed

Volpe, Luigi; Indelli, Pier Francesco; Latella, Leonardo; Poli, Paolo; Yakupoglu, Jale; Marcucci, Massimiliano

2014-01-01

periprosthetic joint infection (PJI) accounts for 25% of failed total knee arthroplasties (TKAs) and 15% of failed total hip arthroplasties (THAs). The purpose of the present study was to design a multidisciplinary diagnostic algorithm to detect a PJI as cause of a painful TKA or THA. from April 2010 to October 2012, 111 patients with suspected PJI were evaluated. The study group comprised 75 females and 36 males with an average age of 71 years (range, 48 to 94 years). Eighty-four patients had a painful THA, while 27 reported a painful TKA. The stepwise diagnostic algorithm, applied in all the patients, included: measurement of serum C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) levels; imaging studies, including standard radiological examination, standard technetium-99m-methylene diphosphonate (MDP) bone scan (if positive, confirmation by LeukoScan was obtained); and joint aspiration with analysis of synovial fluid. following application of the stepwise diagnostic algorithm, 24 out of our 111 screened patients were classified as having a suspected PJI (21.7%). CRP and ESR levels were negative in 84 and positive in 17 cases; 93.7% of the patients had a positive technetium-labeled bone scan, and 23% a positive LeukoScan. Preoperative synovial fluid analysis was positive in 13.5%; analysis of synovial fluid obtained by preoperative aspiration showed a leucocyte count of > 3000 cells μ/l in 52% of the patients. the present study showed that the diagnosis of PJI requires the application of a multimodal diagnostic protocol in order to avoid complications related to surgical revision of a misdiagnosed "silent" PJI. Level IV, therapeutic case series.

Probability scores and diagnostic algorithms in pulmonary embolism: are they followed in clinical practice?

PubMed

Sanjuán, Pilar; Rodríguez-Núñez, Nuria; Rábade, Carlos; Lama, Adriana; Ferreiro, Lucía; González-Barcala, Francisco Javier; Alvarez-Dobaño, José Manuel; Toubes, María Elena; Golpe, Antonio; Valdés, Luis

2014-05-01

Clinical probability scores (CPS) determine the pre-test probability of pulmonary embolism (PE) and assess the need for the tests required in these patients. Our objective is to investigate if PE is diagnosed according to clinical practice guidelines. Retrospective study of clinically suspected PE in the emergency department between January 2010 and December 2012. A D-dimer value ≥ 500 ng/ml was considered positive. PE was diagnosed on the basis of the multislice computed tomography angiography and, to a lesser extent, with other imaging techniques. The CPS used was the revised Geneva scoring system. There was 3,924 cases of suspected PE (56% female). Diagnosis was determined in 360 patients (9.2%) and the incidence was 30.6 cases per 100,000 inhabitants/year. Sensitivity and the negative predictive value of the D-dimer test were 98.7% and 99.2% respectively. CPS was calculated in only 24 cases (0.6%) and diagnostic algorithms were not followed in 2,125 patients (54.2%): in 682 (17.4%) because clinical probability could not be estimated and in 482 (37.6%), 852 (46.4%) and 109 (87.9%) with low, intermediate and high clinical probability, respectively, because the diagnostic algorithms for these probabilities were not applied. CPS are rarely calculated in the diagnosis of PE and the diagnostic algorithm is rarely used in clinical practice. This may result in procedures with potential significant side effects being unnecessarily performed or to a high risk of underdiagnosis. Copyright © 2013 SEPAR. Published by Elsevier Espana. All rights reserved.

A mathematical framework for combining decisions of multiple experts toward accurate and remote diagnosis of malaria using tele-microscopy.

PubMed

Mavandadi, Sam; Feng, Steve; Yu, Frank; Dimitrov, Stoyan; Nielsen-Saines, Karin; Prescott, William R; Ozcan, Aydogan

2012-01-01

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing 'slide-level' diagnosis by using individual 'cell-level' diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.

Design and testing of artifact-suppressed adaptive histogram equalization: a contrast-enhancement technique for display of digital chest radiographs.

PubMed

Rehm, K; Seeley, G W; Dallas, W J; Ovitt, T W; Seeger, J F

1990-01-01

One of the goals of our research in the field of digital radiography has been to develop contrast-enhancement algorithms for eventual use in the display of chest images on video devices with the aim of preserving the diagnostic information presently available with film, some of which would normally be lost because of the smaller dynamic range of video monitors. The ASAHE algorithm discussed in this article has been tested by investigating observer performance in a difficult detection task involving phantoms and simulated lung nodules, using film as the output medium. The results of the experiment showed that the algorithm is successful in providing contrast-enhanced, natural-looking chest images while maintaining diagnostic information. The algorithm did not effect an increase in nodule detectability, but this was not unexpected because film is a medium capable of displaying a wide range of gray levels. It is sufficient at this stage to show that there is no degradation in observer performance. Future tests will evaluate the performance of the ASAHE algorithm in preparing chest images for video display.

Diagnosing breast cancer using Raman spectroscopy: prospective analysis

NASA Astrophysics Data System (ADS)

Haka, Abigail S.; Volynskaya, Zoya; Gardecki, Joseph A.; Nazemi, Jon; Shenk, Robert; Wang, Nancy; Dasari, Ramachandra R.; Fitzmaurice, Maryann; Feld, Michael S.

2009-09-01

We present the first prospective test of Raman spectroscopy in diagnosing normal, benign, and malignant human breast tissues. Prospective testing of spectral diagnostic algorithms allows clinicians to accurately assess the diagnostic information contained in, and any bias of, the spectroscopic measurement. In previous work, we developed an accurate, internally validated algorithm for breast cancer diagnosis based on analysis of Raman spectra acquired from fresh-frozen in vitro tissue samples. We currently evaluate the performance of this algorithm prospectively on a large ex vivo clinical data set that closely mimics the in vivo environment. Spectroscopic data were collected from freshly excised surgical specimens, and 129 tissue sites from 21 patients were examined. Prospective application of the algorithm to the clinical data set resulted in a sensitivity of 83%, a specificity of 93%, a positive predictive value of 36%, and a negative predictive value of 99% for distinguishing cancerous from normal and benign tissues. The performance of the algorithm in different patient populations is discussed. Sources of bias in the in vitro calibration and ex vivo prospective data sets, including disease prevalence and disease spectrum, are examined and analytical methods for comparison provided.

Advanced power system protection and incipient fault detection and protection of spaceborne power systems

NASA Technical Reports Server (NTRS)

Russell, B. Don

1989-01-01

This research concentrated on the application of advanced signal processing, expert system, and digital technologies for the detection and control of low grade, incipient faults on spaceborne power systems. The researchers have considerable experience in the application of advanced digital technologies and the protection of terrestrial power systems. This experience was used in the current contracts to develop new approaches for protecting the electrical distribution system in spaceborne applications. The project was divided into three distinct areas: (1) investigate the applicability of fault detection algorithms developed for terrestrial power systems to the detection of faults in spaceborne systems; (2) investigate the digital hardware and architectures required to monitor and control spaceborne power systems with full capability to implement new detection and diagnostic algorithms; and (3) develop a real-time expert operating system for implementing diagnostic and protection algorithms. Significant progress has been made in each of the above areas. Several terrestrial fault detection algorithms were modified to better adapt to spaceborne power system environments. Several digital architectures were developed and evaluated in light of the fault detection algorithms.

A cDNA microarray gene expression data classifier for clinical diagnostics based on graph theory.

PubMed

Benso, Alfredo; Di Carlo, Stefano; Politano, Gianfranco

2011-01-01

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithms.

A Diagnostic Approach for Electro-Mechanical Actuators in Aerospace Systems

NASA Technical Reports Server (NTRS)

Balaban, Edward; Saxena, Abhinav; Bansal, Prasun; Goebel, Kai Frank; Stoelting, Paul; Curran, Simon

2009-01-01

Electro-mechanical actuators (EMA) are finding increasing use in aerospace applications, especially with the trend towards all all-electric aircraft and spacecraft designs. However, electro-mechanical actuators still lack the knowledge base accumulated for other fielded actuator types, particularly with regard to fault detection and characterization. This paper presents a thorough analysis of some of the critical failure modes documented for EMAs and describes experiments conducted on detecting and isolating a subset of them. The list of failures has been prepared through an extensive Failure Modes and Criticality Analysis (FMECA) reference, literature review, and accessible industry experience. Methods for data acquisition and validation of algorithms on EMA test stands are described. A variety of condition indicators were developed that enabled detection, identification, and isolation among the various fault modes. A diagnostic algorithm based on an artificial neural network is shown to operate successfully using these condition indicators and furthermore, robustness of these diagnostic routines to sensor faults is demonstrated by showing their ability to distinguish between them and component failures. The paper concludes with a roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators.

Intensified Tuberculosis Case Finding among Malnourished Children in Nutritional Rehabilitation Centres of Karnataka, India: Missed Opportunities

PubMed Central

Bhat, Prashant G.; Kumar, Ajay M. V.; Naik, Balaji; Satyanarayana, Srinath; KG, Deepak; Nair, Sreenivas A.; MD, Suryakanth; Heldal, Einar; Enarson, Donald A.; Reid, Anthony J.

2013-01-01

Background Severe acute malnutrition (SAM) is the most serious form of malnutrition affecting children under-five and is associated with many infectious diseases including Tuberculosis (TB). In India, nutritional rehabilitation centres (NRCs) have been recently established for the management of SAM including TB. The National TB Programme (NTP) in India has introduced a revised algorithm for diagnosing paediatric TB. We aimed to examine whether NRCs adhered to these guidelines in diagnosing TB among SAM children. Methods A cross-sectional study involving review of records of all SAM children identified by health workers during 2012 in six tehsils (sub-districts) with NRCs (population: 1.8 million) of Karnataka, India. Results Of 1927 identified SAM children, 1632 (85%) reached NRCs. Of them, 1173 (72%) were evaluated for TB and 19(2%) were diagnosed as TB. Of 1173, diagnostic algorithm was followed in 460 (37%). Among remaining 763 not evaluated as per algorithm, tuberculin skin test alone was conducted in 307 (41%), chest radiography alone in 99 (13%) and no investigations in 337 (45%). The yield of TB was higher among children evaluated as per algorithm (4%) as compared to those who were not (0.3%) (OR: 15.3 [95%CI: 3.5-66.3]). Several operational challenges including non-availability of a full-time paediatrician, non-functioning X-ray machine due to frequent power cuts, use of tuberculin with suboptimal strength and difficulties in adhering to a complex diagnostic algorithm were observed. Conclusion This study showed that TB screening in NRCs was sub-optimal in Karnataka. Some children did not reach the NRC, while many of those who did were either not or sub-optimally evaluated for TB. This study pointed to a number of operational issues that need to be addressed if this collaborative strategy is to identify more TB cases amongst malnourished children in India. PMID:24358350

A structured proteomic approach identifies 14-3-3Sigma as a novel and reliable protein biomarker in panel based differential diagnostics of liver tumors.

PubMed

Reis, Henning; Pütter, Carolin; Megger, Dominik A; Bracht, Thilo; Weber, Frank; Hoffmann, Andreas-C; Bertram, Stefanie; Wohlschläger, Jeremias; Hagemann, Sascha; Eisenacher, Martin; Scherag, André; Schlaak, Jörg F; Canbay, Ali; Meyer, Helmut E; Sitek, Barbara; Baba, Hideo A

2015-06-01

Hepatocellular carcinoma (HCC) is a major lethal cancer worldwide. Despite sophisticated diagnostic algorithms, the differential diagnosis of small liver nodules still is difficult. While imaging techniques have advanced, adjuvant protein-biomarkers as glypican3 (GPC3), glutamine-synthetase (GS) and heat-shock protein 70 (HSP70) have enhanced diagnostic accuracy. The aim was to further detect useful protein-biomarkers of HCC with a structured systematic approach using differential proteome techniques, bring the results to practical application and compare the diagnostic accuracy of the candidates with the established biomarkers. After label-free and gel-based proteomics (n=18 HCC/corresponding non-tumorous liver tissue (NTLT)) biomarker candidates were tested for diagnostic accuracy in immunohistochemical analyses (n=14 HCC/NTLT). Suitable candidates were further tested for consistency in comparison to known protein-biomarkers in HCC (n=78), hepatocellular adenoma (n=25; HCA), focal nodular hyperplasia (n=28; FNH) and cirrhosis (n=28). Of all protein-biomarkers, 14-3-3Sigma (14-3-3S) exhibited the most pronounced up-regulation (58.8×) in proteomics and superior diagnostic accuracy (73.0%) in the differentiation of HCC from non-tumorous hepatocytes also compared to established biomarkers as GPC3 (64.7%) and GS (45.4%). 14-3-3S was part of the best diagnostic three-biomarker panel (GPC3, HSP70, 14-3-3S) for the differentiation of HCC and HCA which is of most important significance. Exclusion of GS and inclusion of 14-3-3S in the panel (>1 marker positive) resulted in a profound increase in specificity (+44.0%) and accuracy (+11.0%) while sensitivity remained stable (96.0%). 14-3-3S is an interesting protein biomarker with the potential to further improve the accuracy of differential diagnostic process of hepatocellular tumors. This article is part of a Special Issue entitled: Medical Proteomics. Copyright © 2014 Elsevier B.V. All rights reserved.

Using the brain's fight-or-flight response for predicting mental illness on the human space flight program

NASA Astrophysics Data System (ADS)

Losik, L.

A predictive medicine program allows disease and illness including mental illness to be predicted using tools created to identify the presence of accelerated aging (a.k.a. disease) in electrical and mechanical equipment. When illness and disease can be predicted, actions can be taken so that the illness and disease can be prevented and eliminated. A predictive medicine program uses the same tools and practices from a prognostic and health management program to process biological and engineering diagnostic data provided in analog telemetry during prelaunch readiness and space exploration missions. The biological and engineering diagnostic data necessary to predict illness and disease is collected from the pre-launch spaceflight readiness activities and during space flight for the ground crew to perform a prognostic analysis on the results from a diagnostic analysis. The diagnostic, biological data provided in telemetry is converted to prognostic (predictive) data using the predictive algorithms. Predictive algorithms demodulate telemetry behavior. They illustrate the presence of accelerated aging/disease in normal appearing systems that function normally. Mental illness can predicted using biological diagnostic measurements provided in CCSDS telemetry from a spacecraft such as the ISS or from a manned spacecraft in deep space. The measurements used to predict mental illness include biological and engineering data from an astronaut's circadian and ultranian rhythms. This data originates deep in the brain that is also damaged from the long-term exposure to cortisol and adrenaline anytime the body's fight or flight response is activated. This paper defines the brain's FOFR; the diagnostic, biological and engineering measurements needed to predict mental illness, identifies the predictive algorithms necessary to process the behavior in CCSDS analog telemetry to predict and thus prevent mental illness from occurring on human spaceflight missions.

Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity at high sensitivity levels (Conference Presentation)

NASA Astrophysics Data System (ADS)

Zhao, Jianhua; Zeng, Haishan; Kalia, Sunil; Lui, Harvey

2017-02-01

Background: Raman spectroscopy is a non-invasive optical technique which can measure molecular vibrational modes within tissue. A large-scale clinical study (n = 518) has demonstrated that real-time Raman spectroscopy could distinguish malignant from benign skin lesions with good diagnostic accuracy; this was validated by a follow-up independent study (n = 127). Objective: Most of the previous diagnostic algorithms have typically been based on analyzing the full band of the Raman spectra, either in the fingerprint or high wavenumber regions. Our objective in this presentation is to explore wavenumber selection based analysis in Raman spectroscopy for skin cancer diagnosis. Methods: A wavenumber selection algorithm was implemented using variably-sized wavenumber windows, which were determined by the correlation coefficient between wavenumbers. Wavenumber windows were chosen based on accumulated frequency from leave-one-out cross-validated stepwise regression or least and shrinkage selection operator (LASSO). The diagnostic algorithms were then generated from the selected wavenumber windows using multivariate statistical analyses, including principal component and general discriminant analysis (PC-GDA) and partial least squares (PLS). A total cohort of 645 confirmed lesions from 573 patients encompassing skin cancers, precancers and benign skin lesions were included. Lesion measurements were divided into training cohort (n = 518) and testing cohort (n = 127) according to the measurement time. Result: The area under the receiver operating characteristic curve (ROC) improved from 0.861-0.891 to 0.891-0.911 and the diagnostic specificity for sensitivity levels of 0.99-0.90 increased respectively from 0.17-0.65 to 0.20-0.75 by selecting specific wavenumber windows for analysis. Conclusion: Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity at high sensitivity levels.

The Hospital Anxiety and Depression Scale (HADS) and the 9-item Patient Health Questionnaire (PHQ-9) as screening instruments for depression in patients with cancer.

PubMed

Hartung, Tim J; Friedrich, Michael; Johansen, Christoffer; Wittchen, Hans-Ulrich; Faller, Herman; Koch, Uwe; Brähler, Elmar; Härter, Martin; Keller, Monika; Schulz, Holger; Wegscheider, Karl; Weis, Joachim; Mehnert, Anja

2017-11-01

Depression screening in patients with cancer is recommended by major clinical guidelines, although the evidence on individual screening tools is limited for this population. Here, the authors assess and compare the diagnostic accuracy of 2 established screening instruments: the depression modules of the 9-item Patient Health Questionnaire (PHQ-9) and the Hospital Anxiety and Depression Scale (HADS-D), in a representative sample of patients with cancer. This multicenter study was conducted with a proportional, stratified, random sample of 2141 patients with cancer across all major tumor sites and treatment settings. The PHQ-9 and HADS-D were assessed and compared in terms of diagnostic accuracy and receiver operating characteristic (ROC) curves for Diagnostic and Statistical Manual of Mental Disorders, 4th edition diagnosis of major depressive disorder using the Composite International Diagnostic Interview for Oncology as the criterion standard. The diagnostic accuracy of the PHQ-9 and HADS-D was fair for diagnosing major depressive disorder, with areas under the ROC curves of 0.78 (95% confidence interval, 0.76-0.79) and 0.75 (95% confidence interval, 0.74-0.77), respectively. The 2 questionnaires did not differ significantly in their areas under the ROC curves (P = .15). The PHQ-9 with a cutoff score ≥7 had the best screening performance, with a sensitivity of 83% (95% confidence interval, 78%-89%) and a specificity of 61% (95% confidence interval, 59%-63%). The American Society of Clinical Oncology guideline screening algorithm had a sensitivity of 44% (95% confidence interval, 36%-51%) and a specificity of 84% (95% confidence interval, 83%-85%). In patients with cancer, the screening performance of both the PHQ-9 and the HADS-D was limited compared with a standardized diagnostic interview. Costs and benefits of routinely screening all patients with cancer should be weighed carefully. Cancer 2017;123:4236-4243. © 2017 American Cancer Society. © 2017 American Cancer Society.

Breast Cancer Diagnostic System Final Report CRADA No. TC02098.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rubenchik, A. M.; DaSilva, L. B.

This was a collaborative effort between Lawrence Livermore National Security, LLC (formerly The Regents of the University of California)/Lawrence Liver more National Laboratory (LLNL) and BioTelligent, Inc. together with a Russian Institution (BioFil, Ltd.), to develop a new system ( diagnostic device, operating procedures, algorithms and software) to accurately distinguish between benign and malignant breast tissue (Breast Cancer Diagnostic System, BCDS).

[EBOLA HEMORRHAGIC FEVER: DIAGNOSTICS, ETIOTROPIC AND PATHOGENETIC THERAPY, PREVENTION].

PubMed

Zhdanov, K V; Zakharenko, S M; Kovalenko, A N; Semenov, A V; Fisun, A Ya

2015-01-01

The data on diagnostics, etiotropic and pathogenetic therapy, prevention of Ebola hemorrhagic fever are presented including diagnostic algorithms for different clinical situations. Fundamentals of pathogenetic therapy are described. Various groups of medications used for antiviral therapy of conditions caused by Ebola virus are characterized. Experimental drugs at different stages of clinical studies are considered along with candidate vaccines being developed for the prevention of the disease.

Toward DSM-V: An Item Response Theory Analysis of the Diagnostic Process for DSM-IV Alcohol Abuse and Dependence in Adolescents

ERIC Educational Resources Information Center

Gelhorn, Heather; Hartman, Christie; Sakai, Joseph; Stallings, Michael; Young, Susan; Rhee, So Hyun; Corley, Robin; Hewitt, John; Hopger, Christian; Crowley, Thomas D.

2008-01-01

Clinical interviews of approximately 5,587 adolescents revealed that DSM-IV diagnostic categories were found to be different in terms of the severity of alcohol use disorders (AUDs). However, a substantial inconsistency and overlap was found in severity of AUDs across categories. The need for an alternative diagnostic algorithm which considers all…

Imbalanced class learning in epigenetics.

PubMed

Haque, M Muksitul; Skinner, Michael K; Holder, Lawrence B

2014-07-01

In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the minority class can be time-consuming or costly or the data may not be easily available. The current study investigates a number of imbalanced class algorithms for solving the imbalanced class distribution present in epigenetic datasets. Epigenetic (DNA methylation) datasets inherently come with few differentially DNA methylated regions (DMR) and with a higher number of non-DMR sites. For this class imbalance problem, a number of algorithms are compared, including the TAN+AdaBoost algorithm. Experiments performed on four epigenetic datasets and several known datasets show that an imbalanced dataset can have similar accuracy as a regular learner on a balanced dataset.

Comparison of four PCR methods for efficient detection of Trypanosoma cruzi in routine diagnostics.

PubMed

Seiringer, Peter; Pritsch, Michael; Flores-Chavez, María; Marchisio, Edoardo; Helfrich, Kerstin; Mengele, Carolin; Hohnerlein, Stefan; Bretzel, Gisela; Löscher, Thomas; Hoelscher, Michael; Berens-Riha, Nicole

2017-07-01

Due to increased migration, Chagas disease has become an international health problem. Reliable diagnosis of chronically infected people is crucial for prevention of non-vectorial transmission as well as treatment. This study compared four distinct PCR methods for detection of Trypanosoma cruzi DNA for the use in well-equipped routine diagnostic laboratories. DNA was extracted of T. cruzi-positive and negative patients' blood samples and cultured T. cruzi, T. rangeli as well as Leishmania spp. One conventional and two real-time PCR methods targeting a repetitive Sat-DNA sequence as well as one conventional PCR method targeting the variable region of the kDNA minicircle were compared for sensitivity, intra- and interassay precision, limit of detection, specificity and cross-reactivity. Considering the performance, costs and ease of use, an algorithm for PCR-diagnosis of patients with a positive serology for T. cruzi antibodies was developed. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Diagnosis of Posttraumatic Stress Disorder in Preschool Children

ERIC Educational Resources Information Center

De Young, Alexandra C.; Kenardy, Justin A.; Cobham, Vanessa E.

2011-01-01

This study investigated the existing diagnostic algorithms for posttraumatic stress disorder (PTSD) to determine the most developmentally sensitive and valid approach for diagnosing this disorder in preschoolers. Participants were 130 parents of unintentionally burned children (1-6 years). Diagnostic interviews were conducted with parents to…

Approximation algorithms for a genetic diagnostics problem.

PubMed

Kosaraju, S R; Schäffer, A A; Biesecker, L G

1998-01-01

We define and study a combinatorial problem called WEIGHTED DIAGNOSTIC COVER (WDC) that models the use of a laboratory technique called genotyping in the diagnosis of an important class of chromosomal aberrations. An optimal solution to WDC would enable us to define a genetic assay that maximizes the diagnostic power for a specified cost of laboratory work. We develop approximation algorithms for WDC by making use of the well-known problem SET COVER for which the greedy heuristic has been extensively studied. We prove worst-case performance bounds on the greedy heuristic for WDC and for another heuristic we call directional greedy. We implemented both heuristics. We also implemented a local search heuristic that takes the solutions obtained by greedy and dir-greedy and applies swaps until they are locally optimal. We report their performance on a real data set that is representative of the options that a clinical geneticist faces for the real diagnostic problem. Many open problems related to WDC remain, both of theoretical interest and practical importance.

Recurrent Pneumonia in Children: A Reasoned Diagnostic Approach and a Single Centre Experience.

PubMed

Montella, Silvia; Corcione, Adele; Santamaria, Francesca

2017-01-29

Recurrent pneumonia (RP), i.e., at least two episodes of pneumonia in one year or three episodes ever with intercritical radiographic clearing of densities, occurs in 7.7%-9% of children with community-acquired pneumonia. In RP, the challenge is to discriminate between children with self-limiting or minor problems, that do not require a diagnostic work-up, and those with an underlying disease. The aim of the current review is to discuss a reasoned diagnostic approach to RP in childhood. Particular emphasis has been placed on which children should undergo a diagnostic work-up and which tests should be performed. A pediatric case series is also presented, in order to document a single centre experience of RP. A management algorithm for the approach to children with RP, based on the evidence from a literature review, is proposed. Like all algorithms, it is not meant to replace clinical judgment, but it should drive physicians to adopt a systematic approach to pediatric RP and provide a useful guide to the clinician.

Clinical diagnosis of ventilator associated pneumonia revisited: comparative validation using immediate post-mortem lung biopsies

PubMed Central

Fabregas, N.; Ewig, S.; Torres, A.; El-Ebiary, M.; Ramirez, J.; de la Bellacasa, J. P.; Bauer, T.; Cabello, H.

1999-01-01

BACKGROUND—A study was undertaken to assess the diagnostic value of different clinical criteria and the impact of microbiological testing on the accuracy of clinical diagnosis of suspected ventilator associated pneumonia (VAP).
METHODS—Twenty five deceased mechanically ventilated patients were studied prospectively. Immediately after death, multiple bilateral lung biopsy specimens (16 specimens/patient) were obtained for histological examination and quantitative lung cultures. The presence of both histological pneumonia and positive lung cultures was used as a reference test.
RESULTS—The presence of infiltrates on the chest radiograph and two of three clinical criteria (leucocytosis, purulent secretions, fever) had a sensitivity of 69% and a specificity of 75%; the corresponding numbers for the clinical pulmonary infection score (CPIS) were 77% and 42%. Non-invasive as well as invasive sampling techniques had comparable values. The combination of all techniques achieved a sensitivity of 85% and a specificity of 50%, and these values remained virtually unchanged despite the presence of previous treatment with antibiotics. When microbiological results were added to clinical criteria, adequate diagnoses originating from microbiological results which might have corrected false positive and false negative clinical judgements (n = 5) were countered by a similar proportion of inadequate diagnoses (n =6).
CONCLUSIONS—Clinical criteria had reasonable diagnostic values. CPIS was not superior to conventional clinical criteria. Non-invasive and invasive sampling techniques had diagnostic values comparable to clinical criteria. An algorithm guiding antibiotic treatment exclusively by microbiological results does not increase the overall diagnostic accuracy and carries the risk of undertreatment.

 PMID:10491448

Diagnostic yield of 90-kVp low-tube-voltage carotid and intracerebral CT-angiography: effects on radiation dose, image quality and diagnostic performance for the detection of carotid stenosis.

PubMed

Leithner, Doris; Wichmann, Julian L; Mahmoudi, Scherwin; Martin, Simon S; Albrecht, Moritz H; Vogl, Thomas J; Scholtz, Jan-Erik

2018-06-01

To investigate the impact of low-tube-voltage 90-kVp acquisition combined with advanced modeled iterative reconstruction algorithm (Admire) on radiation exposure, image quality, artifacts, and assessment of stenosis in carotid and intracranial CT angiography (CTA). Dual-energy CTA studies of 43 patients performed on a third-generation 192-slice dual-source CT were retrospectively evaluated. Intraindividual comparison of 90-kVp and linearly blended 120-kVp equivalent image series (M_0.6, 60% 90-kVp, 40% Sn-150-kVp) was performed. Contrast-to-noise and signal-to-noise ratios of common carotid artery, internal carotid artery, middle cerebral artery, and basilar artery were calculated. Qualitative image analysis included evaluation of artifacts and suitability for angiographical assessment at shoulder level, carotid bifurcation, siphon, and intracranial by three independent radiologists. Detection and quantification of carotid stenosis were performed. Radiation dose was expressed as dose-length product (DLP). Contrast-to-noise values of all arteries were significantly increased in 90-kVp compared to M_0.6 (p < 0.001). Suitability for angiographical evaluation was rated excellent with low artifacts for all levels in both image series. Both 90-kVp and M_0.6 showed excellent accordance for detection and grading of carotid stenosis with almost perfect interobserver agreement (carotid stenoses in 32 of 129 segments; intraclass correlation coefficient, 0.94). dose-length product was reduced by 40.3% in 90-kVp (110.6 ± 32.1 vs 185.4 ± 47.5 mGy·cm, p < 0.001). 90-kVp carotid and intracranial CTA with Admire provides increased quantitative and similarly good qualitative image quality, while reducing radiation exposure substantially compared to M_0.6. Diagnostic performance for arterial stenosis detection and quantification remained excellent. Advances in knowledge: 90-kVp carotid and intracranial CTA with an advanced iterative reconstruction algorithm results in excellent image quality and reduction of radiation exposure without limiting diagnostic performance.

Improved diagnostic differentiation of renal cystic lesions with phase-contrast computed tomography (PCCT)

NASA Astrophysics Data System (ADS)

Noel, Peter B.; Willner, Marian; Fingerle, Alexander; Herzen, Julia; Münzel, Daniela; Hahn, Dieter; Rummeny, Ernst J.; Pfeiffer, Franz

2012-03-01

The diagnostic quality of phase-contrast computed tomography (PCCT) is one the unexplored areas in medical imaging; at the same time, it seems to offer the opportunity as a fast and highly sensitive diagnostic tool. Conventional computed tomography (CT) has had an enormous impact on medicine, while it is limited in soft-tissue contrast. One example that portrays this challenge is the differentiation between benign and malignant renal cysts. In this work we report on a feasibility study to determine the usefulness of PCCT in differentiation of renal cysts. A renal phantom was imaged with a grating-based PCCT system consisting of a standard rotating anode x-ray tube (40 kV, 70 mA) and a Pilatus II photoncounting detector (pixel size: 172 μm). The phantom is composed of a renal equivalent soft-tissue and cystic lesions grouped in non-enhancing cyst and hemorrhage series and an iodine enhancing series. The acquired projection images (absorption and phase-contrast) are reconstructed with a standard filtered backprojection algorithm. For evaluation both reconstructions are compared in respect to contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and subjective image quality. We found that with PCCT a significantly improved differentiation between hemorrhage renal cysts from contrast enhancing malignant cysts is possible. If comparing PCCT and CT with respect to CNR and SNR, PCCT shows significant improvements. In conclusion, PCCT has the potential to improve the diagnostics and characterization of renal cysts without using any contrast agents. These results in combination with a non-synchrotron setup indicate a future paradigm shift in diagnostic computed tomography.

Improved motion compensation in 3D-CT using respiratory-correlated segment reconstruction: diagnostic and radiotherapy applications.

PubMed

Mori, S; Endo, M; Kohno, R; Minohara, S

2006-09-01

Conventional respiratory-gated CT and four-dimensional CT (4DCT) are disadvantaged by their low temporal resolution, which results in the inclusion of anatomic motion-induced artefacts. These represent a significant source of error both in radiotherapy treatment planning for the thorax and upper abdomen and in diagnostic procedures. In particular, temporal resolution and image quality are vitally important to accurate diagnosis and the minimization of planning target volume margin due to respiratory motion. To improve both temporal resolution and signal-to-noise ratio (SNR), we developed a respiratory-correlated segment reconstruction method (RS) and adapted it to the Feldkamp-Davis-Kress algorithm (FDK) with a 256 multidetector row CT (256MDCT). The 256MDCT scans approximately 100 mm in the craniocaudal direction with a 0.5 mm slice thickness in one rotation. Data acquisition for the RS-FDK relies on the assistance of a respiratory sensing system operating in cine scan mode (continuous axial scan with the table stationary). We evaluated the RS-FDK for volume accuracy and image noise in a phantom study with the 256MDCT and compared results with those for a full scan (FS-FDK), which is usually employed in conventional 4DCT and in half scan (HS-FDK). Results showed that the RS-FDK gave a more accurate volume than the others and had the same SNR as the FS-FDK. In a subsequent animal study, we demonstrated a practical sorting process for projection data which was unaffected by variations in respiratory period, and found that the RS-FDK gave the clearest visualization among the three algorithms of the margins of the liver and pulmonary vessels. In summary, the RS-FDK algorithm provides multi-phase images with higher temporal resolution and better SNR. This method should prove useful when combined with new radiotherapeutic and diagnostic techniques.

SU-E-J-218: Novel Validation Paradigm of MRI to CT Deformation of Prostate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Padgett, K; University of Miami School of Medicine - Radiology, Miami, FL; Pirozzi, S

2015-06-15

Purpose: Deformable registration algorithms are inherently difficult to characterize in the multi-modality setting due to a significant differences in the characteristics of the different modalities (CT and MRI) as well as tissue deformations. We present a unique paradigm where this is overcome by utilizing a planning-MRI acquired within an hour of the planning-CT serving as a surrogate for quantifying MRI to CT deformation by eliminating the issues of multi-modality comparisons. Methods: For nine subjects, T2 fast-spin-echo images were acquired at two different time points, the first several weeks prior to planning (diagnostic-MRI) and the second on the same day asmore » the planning-CT (planning-MRI). Significant effort in patient positioning and bowel/bladder preparation was undertaken to minimize distortion of the prostate in all datasets. The diagnostic-MRI was rigidly and deformably aligned to the planning-CT utilizing a commercially available deformable registration algorithm synthesized from local registrations. Additionally, the quality of rigid alignment was ranked by an imaging physicist. The distances between corresponding anatomical landmarks on rigid and deformed registrations (diagnostic-MR to planning-CT) were evaluated. Results: It was discovered that in cases where the rigid registration was of acceptable quality the deformable registration didn’t improve the alignment, this was true of all metrics employed. If the analysis is separated into cases where the rigid alignment was ranked as unacceptable the deformable registration significantly improved the alignment, 4.62mm residual error in landmarks as compared to 5.72mm residual error in rigid alignments with a p-value of 0.0008. Conclusion: This paradigm provides an ideal testing ground for MR to CT deformable registration algorithms by allowing for inter-modality comparisons of multi-modality registrations. Consistent positioning, bowel and bladder preparation may Result in higher quality rigid registrations than typically achieved which limits the impact of deformable registrations. In this study cases where significant differences exist, deformable registrations provide significant value.« less

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

NASA Technical Reports Server (NTRS)

Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

1988-01-01

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

Conversion-Integration of MSFC Nonlinear Signal Diagnostic Analysis Algorithms for Realtime Execution of MSFC's MPP Prototype System

NASA Technical Reports Server (NTRS)

Jong, Jen-Yi

1996-01-01

NASA's advanced propulsion system Small Scale Magnetic Disturbances/Advanced Technology Development (SSME/ATD) has been undergoing extensive flight certification and developmental testing, which involves large numbers of health monitoring measurements. To enhance engine safety and reliability, detailed analysis and evaluation of the measurement signals are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce the risk of catastrophic system failures and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. During the development of SSME, ASRI participated in the research and development of several advanced non- linear signal diagnostic methods for health monitoring and failure prediction in turbomachinery components. However, due to the intensive computational requirement associated with such advanced analysis tasks, current SSME dynamic data analysis and diagnostic evaluation is performed off-line following flight or ground test with a typical diagnostic turnaround time of one to two days. The objective of MSFC's MPP Prototype System is to eliminate such 'diagnostic lag time' by achieving signal processing and analysis in real-time. Such an on-line diagnostic system can provide sufficient lead time to initiate corrective action and also to enable efficient scheduling of inspection, maintenance and repair activities. The major objective of this project was to convert and implement a number of advanced nonlinear diagnostic DSP algorithms in a format consistent with that required for integration into the Vanderbilt Multigraph Architecture (MGA) Model Based Programming environment. This effort will allow the real-time execution of these algorithms using the MSFC MPP Prototype System. ASRI has completed the software conversion and integration of a sequence of nonlinear signal analysis techniques specified in the SOW for real-time execution on MSFC's MPP Prototype. This report documents and summarizes the results of the contract tasks; provides the complete computer source code; including all FORTRAN/C Utilities; and all other utilities/supporting software libraries that are required for operation.

Overcoming barriers to population-based injury research: development and validation of an ICD-10–to–AIS algorithm

PubMed Central

Haas, Barbara; Xiong, Wei; Brennan-Barnes, Maureen; Gomez, David; Nathens, Avery B.

2012-01-01

Background Hospital administrative databases are a useful source of population-level data on injured patients; however, these databases use the International Classification of Diseases (ICD) system, which does not provide a direct means of estimating injury severity. We created and validated a crosswalk to derive Abbreviated Injury Scale (AIS) scores from injury-related diagnostic codes in the tenth revision of the ICD (ICD-10). Methods We assessed the validity of the crosswalk using data from the Ontario Trauma Registry Comprehensive Data Set (OTR-CDS). The AIS and Injury Severity Scores (ISS) derived using the algorithm were compared with those assigned by expert abstractors. We evaluated the ability of the algorithm to identify patients with AIS scores of 3 or greater. We used κ and intraclass correlation coefficients (ICC) as measures of concordance. Results In total, 10 431 patients were identified in the OTR-CDS. The algorithm accurately identified patients with at least 1 AIS score of 3 or greater (κ 0.65), as well as patients with a head AIS score of 3 or greater (κ 0.78). Mapped and abstracted ISS were similar; ICC across the entire cohort was 0.83 (95% confidence interval 0.81–0.84), indicating good agreement. When comparing mapped and abstracted ISS, the difference between scores was 10 or less in 87% of patients. Concordance between mapped and abstracted ISS was similar across strata of age, mechanism of injury and mortality. Conclusion Our ICD-10–to–AIS algorithm produces reliable estimates of injury severity from data available in administrative databases. This algorithm can facilitate the use of administrative data for population-based injury research in jurisdictions using ICD-10. PMID:22269308

Overcoming barriers to population-based injury research: development and validation of an ICD10-to-AIS algorithm.

PubMed

Haas, Barbara; Xiong, Wei; Brennan-Barnes, Maureen; Gomez, David; Nathens, Avery B

2012-02-01

Hospital administrative databases are a useful source of population-level data on injured patients; however, these databases use the International Classification of Diseases (ICD) system, which does not provide a direct means of estimating injury severity. We created and validated a crosswalk to derive Abbreviated Injury Scale (AIS) scores from injury-related diagnostic codes in the tenth revision of the ICD (ICD-10). We assessed the validity of the crosswalk using data from the Ontario Trauma Registry Comprehensive Data Set (OTRCDS). The AIS and Injury Severity Scores (ISS) derived using the algorithm were compared with those assigned by expert abstractors. We evaluated the ability of the algorithm to identify patients with AIS scores of 3 or greater. We used κ and intraclass correlation coefficients (ICC) as measures of concordance. In total, 10 431 patients were identified in the OTRCDS. The algorithm accurately identified patients with at least 1 AIS score of 3 or greater (κ 0.65), as well as patients with a head AIS score of 3 or greater (κ 0.78). Mapped and abstracted ISS were similar; ICC across the entire cohort was 0.83 (95% confidence interval 0.81-0.84), indicating good agreement. When comparing mapped and abstracted ISS, the difference between scores was 10 or less in 87% of patients. Concordance between mapped and abstracted ISS was similar across strata of age, mechanism of injury and mortality. Our ICD-10-to-AIS algorithm produces reliable estimates of injury severity from data available in administrative databases. This algorithm can facilitate the use of administrative data for population-based injury research in jurisdictions using ICD-10.

Effects of Child Characteristics on the Autism Diagnostic Interview-Revised: Implications for Use of Scores as a Measure of ASD Severity

ERIC Educational Resources Information Center

Hus, Vanessa; Lord, Catherine

2013-01-01

The Autism Diagnostic Interview-Revised (ADI-R) is commonly used to inform diagnoses of autism spectrum disorders (ASD). Considering the time dedicated to using the ADI-R, it is of interest to expand the ways in which information obtained from this interview is used. The current study examines how algorithm totals reflecting past (ADI-Diagnostic)…

Diagnosing Sexual Dysfunction in Men and Women: Sexual History Taking and the Role of Symptom Scales and Questionnaires.

PubMed

Hatzichristou, Dimitris; Kirana, Paraskevi-Sofia; Banner, Linda; Althof, Stanley E; Lonnee-Hoffmann, Risa A M; Dennerstein, Lorraine; Rosen, Raymond C

2016-08-01

A detailed sexual history is the cornerstone for all sexual problem assessments and sexual dysfunction diagnoses. Diagnostic evaluation is based on an in-depth sexual history, including sexual and gender identity and orientation, sexual activity and function, current level of sexual function, overall health and comorbidities, partner relationship and interpersonal factors, and the role of cultural and personal expectations and attitudes. To propose key steps in the diagnostic evaluation of sexual dysfunctions, with special focus on the use of symptom scales and questionnaires. Critical assessment of the current literature by the International Consultation on Sexual Medicine committee. A revised algorithm for the management of sexual dysfunctions, level of evidence, and recommendation for scales and questionnaires. The International Consultation on Sexual Medicine proposes an updated algorithm for diagnostic evaluation of sexual dysfunction in men and women, with specific recommendations for sexual history taking and diagnostic evaluation. Standardized scales, checklists, and validated questionnaires are additional adjuncts that should be used routinely in sexual problem evaluation. Scales developed for specific patient groups are included. Results of this evaluation are presented with recommendations for clinical and research uses. Defined principles, an algorithm and a range of scales may provide coherent and evidence based management for sexual dysfunctions. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

A preface on advances in diagnostics for infectious and parasitic diseases: detecting parasites of medical and veterinary importance.

PubMed

Stothard, J Russell; Adams, Emily

2014-12-01

There are many reasons why detection of parasites of medical and veterinary importance is vital and where novel diagnostic and surveillance tools are required. From a medical perspective alone, these originate from a desire for better clinical management and rational use of medications. Diagnosis can be at the individual-level, at close to patient settings in testing a clinical suspicion or at the community-level, perhaps in front of a computer screen, in classification of endemic areas and devising appropriate control interventions. Thus diagnostics for parasitic diseases has a broad remit as parasites are not only tied with their definitive hosts but also in some cases with their vectors/intermediate hosts. Application of current diagnostic tools and decision algorithms in sustaining control programmes, or in elimination settings, can be problematic and even ill-fitting. For example in resource-limited settings, are current diagnostic tools sufficiently robust for operational use at scale or are they confounded by on-the-ground realities; are the diagnostic algorithms underlying public health interventions always understood and well-received within communities which are targeted for control? Within this Special Issue (SI) covering a variety of diseases and diagnostic settings some answers are forthcoming. An important theme, however, throughout the SI is to acknowledge that cross-talk and continuous feedback between development and application of diagnostic tests is crucial if they are to be used effectively and appropriately.

Meta-Review of CSF Core Biomarkers in Alzheimer’s Disease: The State-of-the-Art after the New Revised Diagnostic Criteria

PubMed Central

Ferreira, Daniel; Perestelo-Pérez, Lilisbeth; Westman, Eric; Wahlund, Lars-Olof; Sarría, Antonio; Serrano-Aguilar, Pedro

2014-01-01

Background: Current research criteria for Alzheimer’s disease (AD) include cerebrospinal fluid (CSF) biomarkers into the diagnostic algorithm. However, spreading their use to the clinical routine is still questionable. Objective: To provide an updated, systematic and critical review on the diagnostic utility of the CSF core biomarkers for AD. Data sources: MEDLINE, PreMedline, EMBASE, PsycInfo, CINAHL, Cochrane Library, and CRD. Eligibility criteria: (1a) Systematic reviews with meta-analysis; (1b) Primary studies published after the new revised diagnostic criteria; (2) Evaluation of the diagnostic performance of at least one CSF core biomarker. Results: The diagnostic performance of CSF biomarkers is generally satisfactory. They are optimal for discriminating AD patients from healthy controls. Their combination may also be suitable for mild cognitive impairment (MCI) prognosis. However, CSF biomarkers fail to distinguish AD from other forms of dementia. Limitations: (1) Use of clinical diagnosis as standard instead of pathological postmortem confirmation; (2) variability of methodological aspects; (3) insufficiently long follow-up periods in MCI studies; and (4) lower diagnostic accuracy in primary care compared with memory clinics. Conclusion: Additional work needs to be done to validate the application of CSF core biomarkers as they are proposed in the new revised diagnostic criteria. The use of CSF core biomarkers in clinical routine is more likely if these limitations are overcome. Early diagnosis is going to be of utmost importance when effective pharmacological treatment will be available and the CSF core biomarkers can also be implemented in clinical trials for drug development. PMID:24715863

Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm

PubMed Central

Tra, Viet; Kim, Jaeyoung; Kim, Jong-Myon

2017-01-01

This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum. It is hypothesized that the variation of a bearing’s speed would not alter the overall shape of the AE spectrum rather, it may only scale and translate it. Thus, at different speeds, the same defect would yield SEMs that are scaled and shifted versions of each other. This hypothesis is confirmed by the experimental results, where CNNs trained using the S-DLM algorithm yield significantly better diagnostic performance under variable operating speeds compared to existing methods. In this work, the performance of different training algorithms is also evaluated to select the best training algorithm for the CNNs. The proposed method is used to diagnose both single and compound defects at six different operating speeds. PMID:29211025

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

PubMed Central

Dórea, Fernanda C.; McEwen, Beverly J.; McNab, W. Bruce; Revie, Crawford W.; Sanchez, Javier

2013-01-01

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt–Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel. PMID:23576782

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation.

PubMed

Dórea, Fernanda C; McEwen, Beverly J; McNab, W Bruce; Revie, Crawford W; Sanchez, Javier

2013-06-06

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt-Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.

Modeling of skin cancer dermatoscopy images

NASA Astrophysics Data System (ADS)

Iralieva, Malica B.; Myakinin, Oleg O.; Bratchenko, Ivan A.; Zakharov, Valery P.

2018-04-01

An early identified cancer is more likely to effective respond to treatment and has a less expensive treatment as well. Dermatoscopy is one of general diagnostic techniques for skin cancer early detection that allows us in vivo evaluation of colors and microstructures on skin lesions. Digital phantoms with known properties are required during new instrument developing to compare sample's features with data from the instrument. An algorithm for image modeling of skin cancer is proposed in the paper. Steps of the algorithm include setting shape, texture generation, adding texture and normal skin background setting. The Gaussian represents the shape, and then the texture generation based on a fractal noise algorithm is responsible for spatial chromophores distributions, while the colormap applied to the values corresponds to spectral properties. Finally, a normal skin image simulated by mixed Monte Carlo method using a special online tool is added as a background. Varying of Asymmetry, Borders, Colors and Diameter settings is shown to be fully matched to the ABCD clinical recognition algorithm. The asymmetry is specified by setting different standard deviation values of Gaussian in different parts of image. The noise amplitude is increased to set the irregular borders score. Standard deviation is changed to determine size of the lesion. Colors are set by colormap changing. The algorithm for simulating different structural elements is required to match with others recognition algorithms.

Maximum likelihood phase-retrieval algorithm: applications.

PubMed

Nahrstedt, D A; Southwell, W H

1984-12-01

The maximum likelihood estimator approach is shown to be effective in determining the wave front aberration in systems involving laser and flow field diagnostics and optical testing. The robustness of the algorithm enables convergence even in cases of severe wave front error and real, nonsymmetrical, obscured amplitude distributions.

Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD)

PubMed Central

Quint, Jennifer K; Müllerova, Hana; DiSantostefano, Rachael L; Forbes, Harriet; Eaton, Susan; Hurst, John R; Davis, Kourtney; Smeeth, Liam

2014-01-01

Objectives The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database. Setting 951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard. Primary outcome measure The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed. Results 951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5–92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7–94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2–44.4%). Conclusions Patients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004. PMID:25056980

A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography.

PubMed

Baltzer, Pascal A T; Dietzel, Matthias; Kaiser, Werner A

2013-08-01

In the face of multiple available diagnostic criteria in MR-mammography (MRM), a practical algorithm for lesion classification is needed. Such an algorithm should be as simple as possible and include only important independent lesion features to differentiate benign from malignant lesions. This investigation aimed to develop a simple classification tree for differential diagnosis in MRM. A total of 1,084 lesions in standardised MRM with subsequent histological verification (648 malignant, 436 benign) were investigated. Seventeen lesion criteria were assessed by 2 readers in consensus. Classification analysis was performed using the chi-squared automatic interaction detection (CHAID) method. Results include the probability for malignancy for every descriptor combination in the classification tree. A classification tree incorporating 5 lesion descriptors with a depth of 3 ramifications (1, root sign; 2, delayed enhancement pattern; 3, border, internal enhancement and oedema) was calculated. Of all 1,084 lesions, 262 (40.4 %) and 106 (24.3 %) could be classified as malignant and benign with an accuracy above 95 %, respectively. Overall diagnostic accuracy was 88.4 %. The classification algorithm reduced the number of categorical descriptors from 17 to 5 (29.4 %), resulting in a high classification accuracy. More than one third of all lesions could be classified with accuracy above 95 %. • A practical algorithm has been developed to classify lesions found in MR-mammography. • A simple decision tree consisting of five criteria reaches high accuracy of 88.4 %. • Unique to this approach, each classification is associated with a diagnostic certainty. • Diagnostic certainty of greater than 95 % is achieved in 34 % of all cases.

Fourier analysis algorithm for the posterior corneal keratometric data: clinical usefulness in keratoconus.

PubMed

Sideroudi, Haris; Labiris, Georgios; Georgantzoglou, Kimon; Ntonti, Panagiota; Siganos, Charalambos; Kozobolis, Vassilios

2017-07-01

To develop an algorithm for the Fourier analysis of posterior corneal videokeratographic data and to evaluate the derived parameters in the diagnosis of Subclinical Keratoconus (SKC) and Keratoconus (KC). This was a cross-sectional, observational study that took place in the Eye Institute of Thrace, Democritus University, Greece. Eighty eyes formed the KC group, 55 eyes formed the SKC group while 50 normal eyes populated the control group. A self-developed algorithm in visual basic for Microsoft Excel performed a Fourier series harmonic analysis for the posterior corneal sagittal curvature data. The algorithm decomposed the obtained curvatures into a spherical component, regular astigmatism, asymmetry and higher order irregularities for averaged central 4 mm and for each individual ring separately (1, 2, 3 and 4 mm). The obtained values were evaluated for their diagnostic capacity using receiver operating curves (ROC). Logistic regression was attempted for the identification of a combined diagnostic model. Significant differences were detected in regular astigmatism, asymmetry and higher order irregularities among groups. For the SKC group, the parameters with high diagnostic ability (AUC > 90%) were the higher order irregularities, the asymmetry and the regular astigmatism, mainly in the corneal periphery. Higher predictive accuracy was identified using diagnostic models that combined the asymmetry, regular astigmatism and higher order irregularities in averaged 3and 4 mm area (AUC: 98.4%, Sensitivity: 91.7% and Specificity:100%). Fourier decomposition of posterior Keratometric data provides parameters with high accuracy in differentiating SKC from normal corneas and should be included in the prompt diagnosis of KC. © 2017 The Authors Ophthalmic & Physiological Optics © 2017 The College of Optometrists.

Computer-aided diagnosis workstation and telemedicine network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki

2009-02-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The functions to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and "Success in login" effective. As a result, patients' private information is protected. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

Automated Assessment of Existing Patient's Revised Cardiac Risk Index Using Algorithmic Software.

PubMed

Hofer, Ira S; Cheng, Drew; Grogan, Tristan; Fujimoto, Yohei; Yamada, Takashige; Beck, Lauren; Cannesson, Maxime; Mahajan, Aman

2018-05-25

Previous work in the field of medical informatics has shown that rules-based algorithms can be created to identify patients with various medical conditions; however, these techniques have not been compared to actual clinician notes nor has the ability to predict complications been tested. We hypothesize that a rules-based algorithm can successfully identify patients with the diseases in the Revised Cardiac Risk Index (RCRI). Patients undergoing surgery at the University of California, Los Angeles Health System between April 1, 2013 and July 1, 2016 and who had at least 2 previous office visits were included. For each disease in the RCRI except renal failure-congestive heart failure, ischemic heart disease, cerebrovascular disease, and diabetes mellitus-diagnosis algorithms were created based on diagnostic and standard clinical treatment criteria. For each disease state, the prevalence of the disease as determined by the algorithm, International Classification of Disease (ICD) code, and anesthesiologist's preoperative note were determined. Additionally, 400 American Society of Anesthesiologists classes III and IV cases were randomly chosen for manual review by an anesthesiologist. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve were determined using the manual review as a gold standard. Last, the ability of the RCRI as calculated by each of the methods to predict in-hospital mortality was determined, and the time necessary to run the algorithms was calculated. A total of 64,151 patients met inclusion criteria for the study. In general, the incidence of definite or likely disease determined by the algorithms was higher than that detected by the anesthesiologist. Additionally, in all disease states, the prevalence of disease was always lowest for the ICD codes, followed by the preoperative note, followed by the algorithms. In the subset of patients for whom the records were manually reviewed, the algorithms were generally the most sensitive and the ICD codes the most specific. When computing the modified RCRI using each of the methods, the modified RCRI from the algorithms predicted in-hospital mortality with an area under the receiver operating characteristic curve of 0.70 (0.67-0.73), which compared to 0.70 (0.67-0.72) for ICD codes and 0.64 (0.61-0.67) for the preoperative note. On average, the algorithms took 12.64 ± 1.20 minutes to run on 1.4 million patients. Rules-based algorithms for disease in the RCRI can be created that perform with a similar discriminative ability as compared to physician notes and ICD codes but with significantly increased economies of scale.

Feeding Disorders in Children with Developmental Disabilities.

ERIC Educational Resources Information Center

Schwarz, Steven M.

2003-01-01

This article describes an approach to evaluating and managing feeding disorders in children with developmental disabilities and examines effects of these management strategies on growth and clinical outcomes. A structured approach is stressed and a diagnostic and treatment algorithm is presented. Use with 79 children found that diagnostic-specific…

Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) for Clinical and Research Applications: Recommendations of the International RDC/TMD Consortium Network* and Orofacial Pain Special Interest Group†

PubMed Central

Schiffman, Eric; Ohrbach, Richard; Truelove, Edmond; Look, John; Anderson, Gary; Goulet, Jean-Paul; List, Thomas; Svensson, Peter; Gonzalez, Yoly; Lobbezoo, Frank; Michelotti, Ambra; Brooks, Sharon L.; Ceusters, Werner; Drangsholt, Mark; Ettlin, Dominik; Gaul, Charly; Goldberg, Louis J.; Haythornthwaite, Jennifer A.; Hollender, Lars; Jensen, Rigmor; John, Mike T.; De Laat, Antoon; de Leeuw, Reny; Maixner, William; van der Meulen, Marylee; Murray, Greg M.; Nixdorf, Donald R.; Palla, Sandro; Petersson, Arne; Pionchon, Paul; Smith, Barry; Visscher, Corine M.; Zakrzewska, Joanna; Dworkin, Samuel F.

2015-01-01

Aims The original Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) Axis I diagnostic algorithms have been demonstrated to be reliable. However, the Validation Project determined that the RDC/TMD Axis I validity was below the target sensitivity of ≥ 0.70 and specificity of ≥ 0.95. Consequently, these empirical results supported the development of revised RDC/TMD Axis I diagnostic algorithms that were subsequently demonstrated to be valid for the most common pain-related TMD and for one temporomandibular joint (TMJ) intra-articular disorder. The original RDC/TMD Axis II instruments were shown to be both reliable and valid. Working from these findings and revisions, two international consensus workshops were convened, from which recommendations were obtained for the finalization of new Axis I diagnostic algorithms and new Axis II instruments. Methods Through a series of workshops and symposia, a panel of clinical and basic science pain experts modified the revised RDC/TMD Axis I algorithms by using comprehensive searches of published TMD diagnostic literature followed by review and consensus via a formal structured process. The panel's recommendations for further revision of the Axis I diagnostic algorithms were assessed for validity by using the Validation Project's data set, and for reliability by using newly collected data from the ongoing TMJ Impact Project—the follow-up study to the Validation Project. New Axis II instruments were identified through a comprehensive search of the literature providing valid instruments that, relative to the RDC/TMD, are shorter in length, are available in the public domain, and currently are being used in medical settings. Results The newly recommended Diagnostic Criteria for TMD (DC/TMD) Axis I protocol includes both a valid screener for detecting any pain-related TMD as well as valid diagnostic criteria for differentiating the most common pain-related TMD (sensitivity ≥ 0.86, specificity ≥ 0.98) and for one intra-articular disorder (sensitivity of 0.80 and specificity of 0.97). Diagnostic criteria for other common intra-articular disorders lack adequate validity for clinical diagnoses but can be used for screening purposes. Inter-examiner reliability for the clinical assessment associated with the validated DC/TMD criteria for pain-related TMD is excellent (kappa ≥ 0.85). Finally, a comprehensive classification system that includes both the common and less common TMD is also presented. The Axis II protocol retains selected original RDC/TMD screening instruments augmented with new instruments to assess jaw function as well as behavioral and additional psychosocial factors. The Axis II protocol is divided into screening and comprehensive self-report instrument sets. The screening instruments’ 41 questions assess pain intensity, pain-related disability, psychological distress, jaw functional limitations, and parafunctional behaviors, and a pain drawing is used to assess locations of pain. The comprehensive instruments, composed of 81 questions, assess in further detail jaw functional limitations and psychological distress as well as additional constructs of anxiety and presence of comorbid pain conditions. Conclusion The recommended evidence-based new DC/TMD protocol is appropriate for use in both clinical and research settings. More comprehensive instruments augment short and simple screening instruments for Axis I and Axis II. These validated instruments allow for identification of patients with a range of simple to complex TMD presentations. PMID:24482784

Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

NASA Astrophysics Data System (ADS)

Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y.; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

2016-09-01

We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.

Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data.

PubMed

Horwitz, Leora I; Grady, Jacqueline N; Cohen, Dorothy B; Lin, Zhenqiu; Volpe, Mark; Ngo, Chi K; Masica, Andrew L; Long, Theodore; Wang, Jessica; Keenan, Megan; Montague, Julia; Suter, Lisa G; Ross, Joseph S; Drye, Elizabeth E; Krumholz, Harlan M; Bernheim, Susannah M

2015-10-01

It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions. © 2015 Society of Hospital Medicine.

Emergency ultrasound-based algorithms for diagnosing blunt abdominal trauma.

PubMed

Stengel, Dirk; Rademacher, Grit; Ekkernkamp, Axel; Güthoff, Claas; Mutze, Sven

2015-09-14

Ultrasonography (performed by means of a four-quadrant, focused assessment of sonography for trauma (FAST)) is regarded as a key instrument for the initial assessment of patients with suspected blunt abdominal and thoraco-abdominal trauma in the emergency department setting. FAST has a high specificity but low sensitivity in detecting and excluding visceral injuries. Proponents of FAST argue that ultrasound-based clinical pathways enhance the speed of primary trauma assessment, reduce the number of unnecessary multi-detector computed tomography (MDCT) scans, and enable quicker triage to surgical and non-surgical care. Given the proven accuracy, increasing availability of, and indication for, MDCT among patients with blunt abdominal and multiple injuries, we aimed to compile the best available evidence of the use of FAST-based assessment compared with other primary trauma assessment protocols. To assess the effects of diagnostic algorithms using ultrasonography including in FAST examinations in the emergency department in relation to the early, late, and overall mortality of patients with suspected blunt abdominal trauma. The most recent search was run on 30th June 2015. We searched the Cochrane Injuries Group Specialised Register, The Cochrane Library, MEDLINE (OvidSP), EMBASE (OvidSP), ISI Web of Science (SCI-EXPANDED, SSCI, CPCI-S, and CPSI-SSH), clinical trials registers, and screened reference lists. Trial authors were contacted for further information and individual patient data. We included randomised controlled trials (RCTs). Participants were patients with blunt torso, abdominal, or multiple trauma undergoing diagnostic investigations for abdominal organ injury. The intervention was diagnostic algorithms comprising emergency ultrasonography (US). The control was diagnostic algorithms without US examinations (for example, primary computed tomography (CT) or diagnostic peritoneal lavage (DPL)). Outcomes were mortality, use of CT or invasive procedures (DPL, laparoscopy, laparotomy), and cost-effectiveness. Two authors (DS and CG) independently selected trials for inclusion, assessed methodological quality, and extracted data. Methodological quality was assessed using the Cochrane Collaboration risk of bias tool. Where possible, data were pooled and relative risks (RRs), risk differences (RDs), and weighted mean differences, each with 95% confidence intervals (CIs), were calculated by fixed-effect or random-effects models as appropriate. We identified four studies meeting our inclusion criteria. Overall, trials were of poor to moderate methodological quality. Few trial authors responded to our written inquiries seeking to resolve controversial issues and to obtain individual patient data. Strong heterogeneity amongst the trials prompted discussion between the review authors as to whether the data should or should not be pooled; we decided in favour of a quantitative synthesis to provide a rough impression about the effect sizes achievable with US-based triage algorithms. We pooled mortality data from three trials involving 1254 patients; the RR in favour of the FAST arm was 1.00 (95% CI 0.50 to 2.00). FAST-based pathways reduced the number of CT scans (random-effects model RD -0.52, 95% CI -0.83 to -0.21), but the meaning of this result was unclear. The experimental evidence justifying FAST-based clinical pathways in diagnosing patients with suspected abdominal or multiple blunt trauma remains poor. Because of strong heterogeneity between the trial results, the quantitative information provided by this review may only be used in an exploratory fashion. It is unlikely that FAST will ever be investigated by means of a confirmatory, large-scale RCT in the future. Thus, this Cochrane Review may be regarded as a review which provides the best available evidence for clinical practice guidelines and management recommendations. It can only be concluded from the few head-to-head studies that negative US scans are likely to reduce the incidence of MDCT scans which, given the low sensitivity of FAST (or reliability of negative results), may adversely affect the diagnostic yield of the trauma survey. At best, US has no negative impact on mortality or morbidity. Assuming that major blunt abdominal or multiple trauma is associated with 15% mortality and a CT-based diagnostic work-up is considered the current standard of care, 874, 3495, or 21,838 patients are needed per intervention group to demonstrate non-inferiority of FAST to CT-based algorithms with non-inferiority margins of 5%, 2.5%, and 1%, power of 90%, and a type-I error alpha of 5%.

Automatic tissue characterization from ultrasound imagery

NASA Astrophysics Data System (ADS)

Kadah, Yasser M.; Farag, Aly A.; Youssef, Abou-Bakr M.; Badawi, Ahmed M.

1993-08-01

In this work, feature extraction algorithms are proposed to extract the tissue characterization parameters from liver images. Then the resulting parameter set is further processed to obtain the minimum number of parameters representing the most discriminating pattern space for classification. This preprocessing step was applied to over 120 pathology-investigated cases to obtain the learning data for designing the classifier. The extracted features are divided into independent training and test sets and are used to construct both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms for implementing various classification techniques are presented and tested on the data. The best performance was obtained using a single layer tensor model functional link network. Also, the voting k-nearest neighbor classifier provided comparably good diagnostic rates.

PCA-based artifact removal algorithm for stroke detection using UWB radar imaging.

PubMed

Ricci, Elisa; di Domenico, Simone; Cianca, Ernestina; Rossi, Tommaso; Diomedi, Marina

2017-06-01

Stroke patients should be dispatched at the highest level of care available in the shortest time. In this context, a transportable system in specialized ambulances, able to evaluate the presence of an acute brain lesion in a short time interval (i.e., few minutes), could shorten delay of treatment. UWB radar imaging is an emerging diagnostic branch that has great potential for the implementation of a transportable and low-cost device. Transportability, low cost and short response time pose challenges to the signal processing algorithms of the backscattered signals as they should guarantee good performance with a reasonably low number of antennas and low computational complexity, tightly related to the response time of the device. The paper shows that a PCA-based preprocessing algorithm can: (1) achieve good performance already with a computationally simple beamforming algorithm; (2) outperform state-of-the-art preprocessing algorithms; (3) enable a further improvement in the performance (and/or decrease in the number of antennas) by using a multistatic approach with just a modest increase in computational complexity. This is an important result toward the implementation of such a diagnostic device that could play an important role in emergency scenario.

Accessing primary care Big Data: the development of a software algorithm to explore the rich content of consultation records.

PubMed

MacRae, J; Darlow, B; McBain, L; Jones, O; Stubbe, M; Turner, N; Dowell, A

2015-08-21

To develop a natural language processing software inference algorithm to classify the content of primary care consultations using electronic health record Big Data and subsequently test the algorithm's ability to estimate the prevalence and burden of childhood respiratory illness in primary care. Algorithm development and validation study. To classify consultations, the algorithm is designed to interrogate clinical narrative entered as free text, diagnostic (Read) codes created and medications prescribed on the day of the consultation. Thirty-six consenting primary care practices from a mixed urban and semirural region of New Zealand. Three independent sets of 1200 child consultation records were randomly extracted from a data set of all general practitioner consultations in participating practices between 1 January 2008-31 December 2013 for children under 18 years of age (n=754,242). Each consultation record within these sets was independently classified by two expert clinicians as respiratory or non-respiratory, and subclassified according to respiratory diagnostic categories to create three 'gold standard' sets of classified records. These three gold standard record sets were used to train, test and validate the algorithm. Sensitivity, specificity, positive predictive value and F-measure were calculated to illustrate the algorithm's ability to replicate judgements of expert clinicians within the 1200 record gold standard validation set. The algorithm was able to identify respiratory consultations in the 1200 record validation set with a sensitivity of 0.72 (95% CI 0.67 to 0.78) and a specificity of 0.95 (95% CI 0.93 to 0.98). The positive predictive value of algorithm respiratory classification was 0.93 (95% CI 0.89 to 0.97). The positive predictive value of the algorithm classifying consultations as being related to specific respiratory diagnostic categories ranged from 0.68 (95% CI 0.40 to 1.00; other respiratory conditions) to 0.91 (95% CI 0.79 to 1.00; throat infections). A software inference algorithm that uses primary care Big Data can accurately classify the content of clinical consultations. This algorithm will enable accurate estimation of the prevalence of childhood respiratory illness in primary care and resultant service utilisation. The methodology can also be applied to other areas of clinical care. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Use of a Dual-Antigen Rapid Diagnostic Test to Screen Children for Severe Plasmodium falciparum Malaria in a High-Transmission, Resource-Limited Setting.

PubMed

Boyce, Ross; Reyes, Raquel; Matte, Michael; Ntaro, Moses; Mulogo, Edgar; Siedner, Mark J

2017-10-16

In rural areas, many patients with malaria seek care at peripheral health facilities or community case management programs. While this strategy is effective for the management of uncomplicated malaria, severe malaria necessitates prompt detection and referral to facilities with adequate resources. In this prospective, observational cohort study, we assessed the accuracy of a dual-band (histidine-rich protein-2/pan-lactate dehydrogenase [HRP2/pLDH]) rapid diagnostic test (RDT) to differentiate uncomplicated from severe malaria. We included children aged <12 years who presented to a rural clinic in western Uganda with a positive HRP2 or HRP2/pLDH RDT. We estimated the test characteristics of a dual-antigen (HRP2+/pLDH+) band positive RDT compared to World Health Organization-defined clinical and laboratory criteria to detect severe malaria. A total of 2678 children underwent testing for malaria with an RDT, and 83 (9.0%) satisfied criteria for severe malaria. The sensitivity and specificity of a HRP2+/pLDH+ result for severe malaria was 97.6% (95% confidence interval [CI], 90.8%-99.6%) and 75.6% (95% CI, 73.8%-77.4%), respectively. An HRP2+/pLDH+ result was significantly more sensitive (97.6% vs 68.7%, P < .001) for the detection of severe malaria compared to algorithms that incorporate screening for danger signs. A positive dual-antigen (HRP2/pLDH) RDT has higher sensitivity than the use of clinical manifestations to detect severe malaria, making it a promising tool in the triage of children with malaria in low-resource settings. Additional work is needed to operationalize diagnostic and treatment algorithms that include dual-antigen RDTs to avoid over referral. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Multicenter Evaluation of a 0-Hour/1-Hour Algorithm in the Diagnosis of Myocardial Infarction With High-Sensitivity Cardiac Troponin T.

PubMed

Mueller, Christian; Giannitsis, Evangelos; Christ, Michael; Ordóñez-Llanos, Jorge; deFilippi, Christopher; McCord, James; Body, Richard; Panteghini, Mauro; Jernberg, Tomas; Plebani, Mario; Verschuren, Franck; French, John; Christenson, Robert; Weiser, Silvia; Bendig, Garnet; Dilba, Peter; Lindahl, Bertil

2016-07-01

We aim to prospectively validate the diagnostic accuracy of the recently developed 0-h/1-h algorithm, using high-sensitivity cardiac troponin T (hs-cTnT) for the early rule-out and rule-in of acute myocardial infarction. We enrolled patients presenting with suspected acute myocardial infarction and recent (<6 hours) onset of symptoms to the emergency department in a global multicenter diagnostic study. Hs-cTnT (Roche Diagnostics) and sensitive cardiac troponin I (Siemens Healthcare) were measured at presentation and after 1 hour, 2 hours, and 4 to 14 hours in a central laboratory. Patient triage according to the predefined hs-cTnT 0-hour/1-hour algorithm (hs-cTnT below 12 ng/L and Δ1 hour below 3 ng/L to rule out; hs-cTnT at least 52 ng/L or Δ1 hour at least 5 ng/L to rule in; remaining patients to the "observational zone") was compared against a centrally adjudicated final diagnosis by 2 independent cardiologists (reference standard). The final diagnosis was based on all available information, including coronary angiography and echocardiography results, follow-up data, and serial measurements of sensitive cardiac troponin I, whereas adjudicators remained blinded to hs-cTnT. Among 1,282 patients enrolled, acute myocardial infarction was the final diagnosis for 213 (16.6%) patients. Applying the hs-cTnT 0-hour/1-hour algorithm, 813 (63.4%) patients were classified as rule out, 184 (14.4%) were classified as rule in, and 285 (22.2%) were triaged to the observational zone. This resulted in a negative predictive value and sensitivity for acute myocardial infarction of 99.1% (95% confidence interval [CI] 98.2% to 99.7%) and 96.7% (95% CI 93.4% to 98.7%) in the rule-out zone (7 patients with false-negative results), a positive predictive value and specificity for acute myocardial infarction of 77.2% (95% CI 70.4% to 83.0%) and 96.1% (95% CI 94.7% to 97.2%) in the rule-in zone, and a prevalence of acute myocardial infarction of 22.5% in the observational zone. The hs-cTnT 0-hour/1-hour algorithm performs well for early rule-out and rule-in of acute myocardial infarction. Copyright © 2016 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

Automatic classification of fluorescence and optical diffusion spectroscopy data in neuro-oncology

NASA Astrophysics Data System (ADS)

Savelieva, T. A.; Loshchenov, V. B.; Goryajnov, S. A.; Potapov, A. A.

2018-04-01

The complexity of the biological tissue spectroscopic analysis due to the overlap of biological molecules' absorption spectra, multiple scattering effect, as well as measurement geometry in vivo has caused the relevance of this work. In the neurooncology the problem of tumor boundaries delineation is especially acute and requires the development of new methods of intraoperative diagnosis. Methods of optical spectroscopy allow detecting various diagnostically significant parameters non-invasively. 5-ALA induced protoporphyrin IX is frequently used as fluorescent tumor marker in neurooncology. At the same time analysis of the concentration and the oxygenation level of haemoglobin and significant changes of light scattering in tumor tissues have a high diagnostic value. This paper presents an original method for the simultaneous registration of backward diffuse reflectance and fluorescence spectra, which allows defining all the parameters listed above simultaneously. The clinical studies involving 47 patients with intracranial glial tumors of II-IV Grades were carried out in N.N. Burdenko National Medical Research Center of Neurosurgery. To register the spectral dependences the spectroscopic system LESA- 01-BIOSPEC was used with specially developed w-shaped diagnostic fiber optic probe. The original algorithm of combined spectroscopic signal processing was developed. We have created a software and hardware, which allowed (as compared with the methods currently used in neurosurgical practice) to increase the sensitivity of intraoperative demarcation of intracranial tumors from 78% to 96%, specificity of 60% to 82%. The result of analysis of different techniques of automatic classification shows that in our case the most appropriate is the k Nearest Neighbors algorithm with cubic metrics.

Impact of target organ damage assessment in the evaluation of global risk in patients with essential hypertension.

PubMed

Viazzi, Francesca; Leoncini, Giovanna; Parodi, Denise; Ratto, Elena; Vettoretti, Simone; Vaccaro, Valentina; Parodi, Angelica; Falqui, Valeria; Tomolillo, Cinzia; Deferrari, Giacomo; Pontremoli, Roberto

2005-03-01

Accurate assessment of cardiovascular risk is a key step toward optimizing the treatment of hypertensive patients. We analyzed the impact and cost-effectiveness of routine, thorough assessment of target organ damage (TOD) in evaluating risk profile in hypertension. A total of 380 never-treated patients with essential hypertension underwent routine work-up plus evaluation of albuminuria and ultrasonography of cardiac and vascular structures. The impact of these tests on risk stratification, as indicated by European Society of Hypertension-European Society of Cardiology guidelines, was assessed in light of their cost and sensitivity. The combined use of all of these tests greatly improved the detection of TOD, therefore leading to the identification of a higher percentage of patients who were at high/very high risk, as compared with those who were detected by routine clinical work-up (73% instead of 42%; P < 0.0001). Different signs of TOD only partly cluster within the same subgroup of patients; thus, all three tests should be performed to maximize the sensitivity of the evaluation process. The diagnostic algorithm yielding the lowest cost per detected case of TOD is the search for microalbuminuria, followed by echocardiography and then carotid ultrasonography. Adopting lower cut-off values to define microalbuminuria allows us to optimize further the cost-effectiveness of diagnostic algorithms. In conclusion, because of its low cost and widespread availability, measuring albuminuria is an attractive and cost-effective screening test that is especially suitable as the first step in the large-scale diagnostic work-up of hypertensive patients.

Diagnostic power of diffuse reflectance spectroscopy for targeted detection of breast lesions with microcalcifications

PubMed Central

Soares, Jaqueline S.; Barman, Ishan; Dingari, Narahara Chari; Volynskaya, Zoya; Liu, Wendy; Klein, Nina; Plecha, Donna; Dasari, Ramachandra R.; Fitzmaurice, Maryann

2013-01-01

Microcalcifications geographically target the location of abnormalities within the breast and are of critical importance in breast cancer diagnosis. However, despite stereotactic guidance, core needle biopsy fails to retrieve microcalcifications in up to 15% of patients. Here, we introduce an approach based on diffuse reflectance spectroscopy for detection of microcalcifications that focuses on variations in optical absorption stemming from the calcified clusters and the associated cross-linking molecules. In this study, diffuse reflectance spectra are acquired ex vivo from 203 sites in fresh biopsy tissue cores from 23 patients undergoing stereotactic breast needle biopsies. By correlating the spectra with the corresponding radiographic and histologic assessment, we have developed a support vector machine-derived decision algorithm, which shows high diagnostic power (positive predictive value and negative predictive value of 97% and 88%, respectively) for diagnosis of lesions with microcalcifications. We further show that these results are robust and not due to any spurious correlations. We attribute our findings to the presence of proteins (such as elastin), and desmosine and isodesmosine cross-linkers in the microcalcifications. It is important to note that the performance of the diffuse reflectance decision algorithm is comparable to one derived from the corresponding Raman spectra, and the considerably higher intensity of the reflectance signal enables the detection of the targeted lesions in a fraction of the spectral acquisition time. Our findings create a unique landscape for spectroscopic validation of breast core needle biopsy for detection of microcalcifications that can substantially improve the likelihood of an adequate, diagnostic biopsy in the first attempt. PMID:23267090

Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.

PubMed

Marchetti, Michael A; Codella, Noel C F; Dusza, Stephen W; Gutman, David A; Helba, Brian; Kalloo, Aadi; Mishra, Nabin; Carrera, Cristina; Celebi, M Emre; DeFazio, Jennifer L; Jaimes, Natalia; Marghoob, Ashfaq A; Quigley, Elizabeth; Scope, Alon; Yélamos, Oriol; Halpern, Allan C

2018-02-01

Computer vision may aid in melanoma detection. We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images. We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant. The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001). The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice. Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists. Copyright © 2017 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Monitoring endemic livestock diseases using laboratory diagnostic data: A simulation study to evaluate the performance of univariate process monitoring control algorithms.

PubMed

Lopes Antunes, Ana Carolina; Dórea, Fernanda; Halasa, Tariq; Toft, Nils

2016-05-01

Surveillance systems are critical for accurate, timely monitoring and effective disease control. In this study, we investigated the performance of univariate process monitoring control algorithms in detecting changes in seroprevalence for endemic diseases. We also assessed the effect of sample size (number of sentinel herds tested in the surveillance system) on the performance of the algorithms. Three univariate process monitoring control algorithms were compared: Shewart p Chart(1) (PSHEW), Cumulative Sum(2) (CUSUM) and Exponentially Weighted Moving Average(3) (EWMA). Increases in seroprevalence were simulated from 0.10 to 0.15 and 0.20 over 4, 8, 24, 52 and 104 weeks. Each epidemic scenario was run with 2000 iterations. The cumulative sensitivity(4) (CumSe) and timeliness were used to evaluate the algorithms' performance with a 1% false alarm rate. Using these performance evaluation criteria, it was possible to assess the accuracy and timeliness of the surveillance system working in real-time. The results showed that EWMA and PSHEW had higher CumSe (when compared with the CUSUM) from week 1 until the end of the period for all simulated scenarios. Changes in seroprevalence from 0.10 to 0.20 were more easily detected (higher CumSe) than changes from 0.10 to 0.15 for all three algorithms. Similar results were found with EWMA and PSHEW, based on the median time to detection. Changes in the seroprevalence were detected later with CUSUM, compared to EWMA and PSHEW for the different scenarios. Increasing the sample size 10 fold halved the time to detection (CumSe=1), whereas increasing the sample size 100 fold reduced the time to detection by a factor of 6. This study investigated the performance of three univariate process monitoring control algorithms in monitoring endemic diseases. It was shown that automated systems based on these detection methods identified changes in seroprevalence at different times. Increasing the number of tested herds would lead to faster detection. However, the practical implications of increasing the sample size (such as the costs associated with the disease) should also be taken into account. Copyright © 2016 Elsevier B.V. All rights reserved.

Effect of absorption on nonlinear propagation of short ultrasound pulses generated by rectangular transducers

NASA Astrophysics Data System (ADS)

Khokhlova, Vera A.; Ponomaryov, Anatoly E.; Averkiou, Michalakis A.; Crum, Lawrence A.

2002-11-01

A numerical solution of the KZK-type parabolic nonlinear evolution equation is presented for finite-amplitude sound beams radiated by rectangular sources. The initial acoustic waveform is a short tone burst, similar to those used in diagnostic ultrasound. The generation of higher harmonic components and their spatial structure are investigated for media similar to tissue with various frequency dependent absorption properties. Nonlinear propagation in a thermoviscous fluid with a quadratic frequency law of absorption is compared to that in tissue with a nearly linear frequency law of absorption. The algorithm is based on that originally developed by Lee and Hamilton [J. Acoust. Soc. Am. 97, 906-917 (1995)] to model circular sources. The algorithm is generalized for two-dimensional sources without axial symmetry. The diffraction integral is adapted in the time-domain for two dimensions with the implicit backward finite difference (IBFD) scheme in the nearfield and with the alternate direction implicit (ADI) method at longer distances. Arbitrary frequency dependence of absorption is included in this model and solved in the frequency-domain using the FFT technique. The results of simulation may be used to better understand the nonlinear beam structure for tissue harmonic imaging in modern medical diagnostic scanners. [Work supported by CRDF and RFBR.

A System for Heart Sounds Classification

PubMed Central

Redlarski, Grzegorz; Gradolewski, Dawid; Palkowski, Aleksander

2014-01-01

The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases – one of the major causes of death around the globe – a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability. PMID:25393113

A structured reading algorithm improves telemetric detection of atrial fibrillation after acute ischemic stroke.

PubMed

Kallmünzer, Bernd; Breuer, Lorenz; Hering, Christiane; Raaz-Schrauder, Dorette; Kollmar, Rainer; Huttner, Hagen B; Schwab, Stefan; Köhrmann, Martin

2012-04-01

Anticoagulation is a highly effective secondary prevention in patients with cardioembolic stroke and atrial fibrillation/flutter (AF). However, the condition remains underdiagnosed, because paroxysmal AF may be missed by diagnostic tests in the acute phase. In this study, the sensitivity of AF detection was assessed for serial electrocardiographic recordings and continuous stroke unit telemetric monitoring with or without a structured algorithm to analyze telemetric data (SEA-AF). Three hundred forty-six consecutive patients with acute ischemic stroke were prospectively included and subjected to standard telemetric monitoring. In addition, telemetric data were separately analyzed following SEA-AF, consisting of a structured evaluation of episodes with high risk for AF and a chronological beat-to-beat screening of the full registration. Serial electrocardiograms were conducted in 24-hour intervals. Median effective telemetry monitoring time was 75.5 hours (interquartile range 64-86 hours). Overall, AF was diagnosed in 119 of 346 patients (34.4%). The structured reading algorithm was the most sensitive method to detected AF. Conventional telemetry and serial electrocardiographic assessments were less effective. However, only 35% of patients with previously documented paroxysmal AF and negative baseline electrocardiogram demonstrated AF episodes during monitoring. Continuous stroke unit telemetry using SEA-AF shows a significantly higher detection rate for AF compared with daily electrocardiographic assessments and standard telemetry without structured reading. The low overall probability to detect paroxysmal AF with either method during the first days after stroke demonstrates the urgent need for complementary diagnostic strategies such as long-term monitoring and frequent follow-up assessments. Clinical Trial Registration- URL: www.clinicaltrials.gov. Unique identifier: NCT01177748.

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis.

PubMed

Kavitha, Muthu Subash; Asano, Akira; Taguchi, Akira; Heo, Min-Suk

2013-09-01

To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

Introducing minimum Fisher regularisation tomography to AXUV and soft x-ray diagnostic systems of the COMPASS tokamak

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mlynar, J.; Weinzettl, V.; Imrisek, M.

2012-10-15

The contribution focuses on plasma tomography via the minimum Fisher regularisation (MFR) algorithm applied on data from the recently commissioned tomographic diagnostics on the COMPASS tokamak. The MFR expertise is based on previous applications at Joint European Torus (JET), as exemplified in a new case study of the plasma position analyses based on JET soft x-ray (SXR) tomographic reconstruction. Subsequent application of the MFR algorithm on COMPASS data from cameras with absolute extreme ultraviolet (AXUV) photodiodes disclosed a peaked radiating region near the limiter. Moreover, its time evolution indicates transient plasma edge cooling following a radial plasma shift. In themore » SXR data, MFR demonstrated that a high resolution plasma positioning independent of the magnetic diagnostics would be possible provided that a proper calibration of the cameras on an x-ray source is undertaken.« less

A wave model of refraction of laser beams with a discrete change in intensity in their cross section and their application for diagnostics of extended nonstationary phase objects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raskovskaya, I L

2015-08-31

A beam model with a discrete change in the cross-sectional intensity is proposed to describe refraction of laser beams formed on the basis of diffractive optical elements. In calculating the wave field of the beams of this class under conditions of strong refraction, in contrast to the traditional asymptotics of geometric optics which assumes a transition to the infinite limits of integration and obtaining an analytical solution, it is proposed to calculate the integral in the vicinity of stationary points. This approach allows the development of a fast algorithm for correct calculation of the wave field of the laser beamsmore » that are employed in probing and diagnostics of extended optically inhomogeneous media. Examples of the algorithm application for diagnostics of extended nonstationary objects in liquid are presented. (laser beams)« less

Algorithms for the diagnosis and treatment of restless legs syndrome in primary care

PubMed Central

2011-01-01

Background Restless legs syndrome (RLS) is a neurological disorder with a lifetime prevalence of 3-10%. in European studies. However, the diagnosis of RLS in primary care remains low and mistreatment is common. Methods The current article reports on the considerations of RLS diagnosis and management that were made during a European Restless Legs Syndrome Study Group (EURLSSG)-sponsored task force consisting of experts and primary care practioners. The task force sought to develop a better understanding of barriers to diagnosis in primary care practice and overcome these barriers with diagnostic and treatment algorithms. Results The barriers to diagnosis identified by the task force include the presentation of symptoms, the language used to describe them, the actual term "restless legs syndrome" and difficulties in the differential diagnosis of RLS. Conclusion The EURLSSG task force reached a consensus and agreed on the diagnostic and treatment algorithms published here. PMID:21352569

[Multi-centre clinical assessment of the Russian language version of the Diagnostic Interview for Psychoses].

PubMed

Smirnova, D A; Petrova, N N; Pavlichenko, A V; Martynikhin, I A; Dorofeikova, M V; Eremkin, V I; Izmailova, O V; Osadshiy, Yu Yu; Romanov, D V; Ubeikon, D A; Fedotov, I A; Sheifer, M S; Shustov, A D; Yashikhina, A A; Clark, M; Badcock, J; Watterreus, A; Morgan, V; Jablensky, A

2018-01-01

The Diagnostic Interview for Psychoses (DIP) was developed to enhance the quality of diagnostic assessment of psychotic disorders. The aim of the study was the adaptation of the Russian language version and evaluation of its validity and reliability. Ninety-eight patients with psychotic disorders (89 video recordings) were assessed by 12 interviewers using the Russian version of DIP at 7 clinical sites (in 6 cities of the Russian Federation). DIP ratings on 32 cases of a randomized case sample were made by 9 interviewers and the inter-rater reliability was compared with the researchers' DIP ratings. Overall pairwise agreement and Cohen's kappa were calculated. Diagnostic validity was evaluated on the basis of comparing the researchers' ratings using the Russian version of DIP with the 'gold standard' ratings of the same 62 clinical cases from the Western Australia Family Study Schizophrenia (WAFSS). The mean duration of the interview was 47±21 minutes. The Kappa statistic demonstrated a significant or almost perfect level of agreement on the majority of DIP items (84.54%) and a significant agreement for the ICD-10 diagnoses generated by the DIP computer diagnostic algorithm (κ=0.68; 95% CI 0.53,0.93). The level of agreement on the researchers' diagnoses was considerably lower (κ=0.31; 95% CI 0.06,0.56). The agreement on affective and positive psychotic symptoms was significantly higher than agreement on negative symptoms (F(2,44)=20.72, p<0.001, η2=0.485). The diagnostic validity of the Russian language version of DIP was confirmed by 73% (45/62) of the Russian DIP diagnoses matching the original WAFSS diagnoses. Among the mismatched diagnoses were 80 cases with a diagnosis of F20 Schizophrenia in the medical documentation compared to the researchers' F20 diagnoses in only 68 patients and in 62 of the DIP computerized diagnostic outputs. The reported level of subjective difficulties experienced when using the DIP was low to moderate. The results of the study confirm the validity and reliability of the Russian version of the DIP for evaluating psychotic disorders. DIP can be recommended for use in education and training, clinical practice and research as an important diagnostic resource.

Cancer Diagnosis Epigenomics Scientific Workflow Scheduling in the Cloud Computing Environment Using an Improved PSO Algorithm

PubMed

N, Sadhasivam; R, Balamurugan; M, Pandi

2018-01-27

Objective: Epigenetic modifications involving DNA methylation and histone statud are responsible for the stable maintenance of cellular phenotypes. Abnormalities may be causally involved in cancer development and therefore could have diagnostic potential. The field of epigenomics refers to all epigenetic modifications implicated in control of gene expression, with a focus on better understanding of human biology in both normal and pathological states. Epigenomics scientific workflow is essentially a data processing pipeline to automate the execution of various genome sequencing operations or tasks. Cloud platform is a popular computing platform for deploying large scale epigenomics scientific workflow. Its dynamic environment provides various resources to scientific users on a pay-per-use billing model. Scheduling epigenomics scientific workflow tasks is a complicated problem in cloud platform. We here focused on application of an improved particle swam optimization (IPSO) algorithm for this purpose. Methods: The IPSO algorithm was applied to find suitable resources and allocate epigenomics tasks so that the total cost was minimized for detection of epigenetic abnormalities of potential application for cancer diagnosis. Result: The results showed that IPSO based task to resource mapping reduced total cost by 6.83 percent as compared to the traditional PSO algorithm. Conclusion: The results for various cancer diagnosis tasks showed that IPSO based task to resource mapping can achieve better costs when compared to PSO based mapping for epigenomics scientific application workflow. Creative Commons Attribution License

Case-Deletion Diagnostics for Nonlinear Structural Equation Models

ERIC Educational Resources Information Center

Lee, Sik-Yum; Lu, Bin

2003-01-01

In this article, a case-deletion procedure is proposed to detect influential observations in a nonlinear structural equation model. The key idea is to develop the diagnostic measures based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. An one-step pseudo approximation is proposed to reduce the…

Sequential Test Strategies for Multiple Fault Isolation

NASA Technical Reports Server (NTRS)

Shakeri, M.; Pattipati, Krishna R.; Raghavan, V.; Patterson-Hine, Ann; Kell, T.

1997-01-01

In this paper, we consider the problem of constructing near optimal test sequencing algorithms for diagnosing multiple faults in redundant (fault-tolerant) systems. The computational complexity of solving the optimal multiple-fault isolation problem is super-exponential, that is, it is much more difficult than the single-fault isolation problem, which, by itself, is NP-hard. By employing concepts from information theory and Lagrangian relaxation, we present several static and dynamic (on-line or interactive) test sequencing algorithms for the multiple fault isolation problem that provide a trade-off between the degree of suboptimality and computational complexity. Furthermore, we present novel diagnostic strategies that generate a static diagnostic directed graph (digraph), instead of a static diagnostic tree, for multiple fault diagnosis. Using this approach, the storage complexity of the overall diagnostic strategy reduces substantially. Computational results based on real-world systems indicate that the size of a static multiple fault strategy is strictly related to the structure of the system, and that the use of an on-line multiple fault strategy can diagnose faults in systems with as many as 10,000 failure sources.

When the bell tolls on Bell's palsy: finding occult malignancy in acute-onset facial paralysis.

PubMed

Quesnel, Alicia M; Lindsay, Robin W; Hadlock, Tessa A

2010-01-01

This study reports 4 cases of occult parotid malignancy presenting with sudden-onset facial paralysis to demonstrate that failure to regain tone 6 months after onset distinguishes these patients from Bell's palsy patients with delayed recovery and to propose a diagnostic algorithm for this subset of patients. A case series of 4 patients with occult parotid malignancies presenting with acute-onset unilateral facial paralysis is reported. Initial imaging on all 4 patients did not demonstrate a parotid mass. Diagnostic delays ranged from 7 to 36 months from time of onset of facial paralysis to time of diagnosis of parotid malignancy. Additional physical examination findings, especially failure to regain tone, as well as properly protocolled radiologic studies reviewed with dedicated head and neck radiologists, were helpful in arriving at the diagnosis. An algorithm to minimize diagnostic delays in this subset of acute facial paralysis patients is presented. Careful attention to facial tone, in addition to movement, is important in the diagnostic evaluation of acute-onset facial paralysis. Copyright 2010 Elsevier Inc. All rights reserved.

External validation of heart-type fatty acid binding protein, high-sensitivity cardiac troponin, and electrocardiography as rule-out for acute myocardial infarction.

PubMed

Van Hise, Christopher B; Greenslade, Jaimi H; Parsonage, William; Than, Martin; Young, Joanna; Cullen, Louise

2018-02-01

To externally validate a clinical decision rule incorporating heart fatty acid binding protein (h-FABP), high-sensitivity troponin (hs-cTn) and electrocardiogram (ECG) for the detection of acute myocardial infarction (AMI) on presentation to the Emergency Department. We also investigated whether this clinical decision rule improved identification of AMI over algorithms incorporating hs-cTn and ECG only. This study included data from 789 patients from the Brisbane ADAPT cohort and 441 patients from the Christchurch TIMI RCT cohort. The primary outcome was index AMI. Sensitivity, specificity, positive predictive value and negative predictive value were used to assess the diagnostic accuracy of the algorithms. 1230 patients were recruited, including 112 (9.1%) with AMI. The algorithm including h-FABP and hs-cTnT had 100% sensitivity and 32.4% specificity. The algorithm utilising h-FABP and hs-cTnI had similar sensitivity (99.1%) and higher specificity (43.4%). The hs-cTnI and hs-cTnT algorithms without h-FABP both had a sensitivity of 98.2%; a result that was not significantly different from either algorithm incorporating h-FABP. Specificity was higher for the hs-cTnI algorithm (68.1%) compared to the hs-cTnT algorithm (33.0%). The specificity of the algorithm incorporating hs-cTnI alone was also significantly higher than both of the algorithms incorporating h-FABP (p<0.01). For patients presenting to the Emergency Department with chest pain, an algorithm incorporating h-FABP, hs-cTn and ECG has high accuracy and can rule out up to 40% of patients. An algorithm incorporating only hs-cTn and ECG has similar sensitivity and may rule out a higher proportion of patients. Each of the algorithms can be used to safely identify patients as low risk for AMI on presentation to the Emergency Department. Copyright © 2017 The Canadian Society of Clinical Chemists. All rights reserved.

Developing a new diagnostic algorithm for human papilloma virus associated oropharyngeal carcinoma: an investigation of HPV DNA assays.

PubMed

Cohen, Natasha; Gupta, Michael; Doerwald-Munoz, Lilian; Jang, Dan; Young, James Edward Massey; Archibald, Stuart; Jackson, Bernard; Lee, Jenny; Chernesky, Max

2017-02-13

Human papilloma virus (HPV) has been implicated in the development of a large proportion of oropharyngeal squamous cell carcinoma (OPSCC). Current techniques used to diagnose HPV etiology require histopathologic analysis. We aim to investigate the diagnostic accuracy of a new application non-histopathologic diagnostic tests to help assist diagnosis of HPV-related oropharyngeal tumors. Patients with OPSCC with nodal metastasis were consecutively recruited from a multidisciplinary cancer clinic. Appropriate samples were collected and analyzed. The various tests examined included COBAS® 4800, Cervista® HR and Genotyping. These tests were compared to p16 staining, which was used as the diagnostic standard. StataIC 14.2 was used to perform analysis, including sensitivity, specificity and receiver operator characteristic [ROC] curves. The COBAS® FNA (area under ROC 0.863) and saliva (area under ROC 0.847) samples performed well in diagnosing HPV positive and negative tumors. Samples tested with Cervista® did not corroborate p16 status reliably. We were able to increase the diagnostic yield of the COBAS® FNA samples by applying the results of the saliva test to negative FNA samples which correctly identified 11 additional p16 positive tumors (area under ROC 0.915). Surrogate testing for HPV using alternate methods is feasible and closely predicts the results of standard diagnostic methods. In the future, these could minimize invasive procedures for diagnosing HPV-related oropharyngeal cancer, but also help to diagnose and treat patients with unknown primaries.

Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators.

PubMed

Karayiannis, N B

2000-01-01

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.

Comparative analysis of diagnostic performance, feasibility and cost of different test-methods for thyroid nodules with indeterminate cytology

PubMed Central

Sciacchitano, Salvatore; Lavra, Luca; Ulivieri, Alessandra; Magi, Fiorenza; De Francesco, Gian Paolo; Bellotti, Carlo; Salehi, Leila B.; Trovato, Maria; Drago, Carlo; Bartolazzi, Armando

2017-01-01

Since it is impossible to recognize malignancy at fine needle aspiration (FNA) cytology in indeterminate thyroid nodules, surgery is recommended for all of them. However, cancer rate at final histology is <30%. Many different test-methods have been proposed to increase diagnostic accuracy in such lesions, including Galectin-3-ICC (GAL-3-ICC), BRAF mutation analysis (BRAF), Gene Expression Classifier (GEC) alone and GEC+BRAF, mutation/fusion (M/F) panel, alone, M/F panel+miRNA GEC, and M/F panel by next generation sequencing (NGS), FDG-PET/CT, MIBI-Scan and TSHR mRNA blood assay. We performed systematic reviews and meta-analyses to compare their features, feasibility, diagnostic performance and cost. GEC, GEC+BRAF, M/F panel+miRNA GEC and M/F panel by NGS were the best in ruling-out malignancy (sensitivity = 90%, 89%, 89% and 90% respectively). BRAF and M/F panel alone and by NGS were the best in ruling-in malignancy (specificity = 100%, 93% and 93%). The M/F by NGS showed the highest accuracy (92%) and BRAF the highest diagnostic odds ratio (DOR) (247). GAL-3-ICC performed well as rule-out (sensitivity = 83%) and rule-in test (specificity = 85%), with good accuracy (84%) and high DOR (27) and is one of the cheapest (113 USD) and easiest one to be performed in different clinical settings. In conclusion, the more accurate molecular-based test-methods are still expensive and restricted to few, highly specialized and centralized laboratories. GAL-3-ICC, although limited by some false negatives, represents the most suitable screening test-method to be applied on a large-scale basis in the diagnostic algorithm of indeterminate thyroid lesions. PMID:28472764

Computational Algorithmization: Limitations in Problem Solving Skills in Computational Sciences Majors at University of Oriente

ERIC Educational Resources Information Center

Castillo, Antonio S.; Berenguer, Isabel A.; Sánchez, Alexander G.; Álvarez, Tomás R. R.

2017-01-01

This paper analyzes the results of a diagnostic study carried out with second year students of the computational sciences majors at University of Oriente, Cuba, to determine the limitations that they present in computational algorithmization. An exploratory research was developed using quantitative and qualitative methods. The results allowed…

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review

PubMed Central

van Mourik, Maaike S M; van Duijn, Pleun Joppe; Moons, Karel G M; Bonten, Marc J M; Lee, Grace M

2015-01-01

Objective Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI. Methods Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995–2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics. Results 57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances. Conclusions Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative. PMID:26316651

Computer-aided diagnosis workstation and teleradiology network system for chest diagnosis using the web medical image conference system with a new information security solution

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kaneko, Masahiro; Kakinuma, Ryutaro; Moriyama, Noriyuki

2010-03-01

Diagnostic MDCT imaging requires a considerable number of images to be read. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. Because of such a background, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis. We also have developed the teleradiology network system by using web medical image conference system. In the teleradiology network system, the security of information network is very important subjects. Our teleradiology network system can perform Web medical image conference in the medical institutions of a remote place using the web medical image conference system. We completed the basic proof experiment of the web medical image conference system with information security solution. We can share the screen of web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with the workstation that builds in some diagnostic assistance methods. Biometric face authentication used on site of teleradiology makes "Encryption of file" and "Success in login" effective. Our Privacy and information security technology of information security solution ensures compliance with Japanese regulations. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new teleradiology network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our teleradiology network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

[Managment of acute low back pain without trauma - an algorithm].

PubMed

Melcher, Carolin; Wegener, Bernd; Jansson, Volkmar; Mutschler, Wolf; Kanz, Karl-Georg; Birkenmaier, Christof

2018-05-14

Low back pain is a common problem for primary care providers, outpatient clinics and A&E departments. The predominant symptoms are those of so-called "unspecific back pain", but serious pathologies can be concealed by the clinical signs. Especially less experienced colleagues have problems in treating these patients, as - despite the multitude of recommendations and guidelines - there is no generally accepted algorithm. After a literature search (Medline/Cochrane), 158 articles were selected from 15,000 papers and classified according to their level of evidence. These were attuned to the clinical guidelines of the orthopaedic and pain-physician associations in Europe, North America and overseas and the experience of specialists at LMU Munich, in order to achieve consistency with literature recommendations, as well as feasibility in everyday clinical work and optimised with practical relevance. An algorithm was formed to provide the crucial differential diagnosis of lumbar back pain according to its clinical relevance and to provide a plan of action offering reasonable diagnostic and therapeutic steps. As a consequence of distinct binary decisions, low back patients should be treated at any given time according to the guidelines, with emergencies detected, unnecessary diagnostic testing and interventions averted and reasonable treatment initiated pursuant to the underlying pathology. In the context of the available evidence, a clinical algorithm has been developed that translates the complex diagnostic testing of acute low back pain into a transparent, structured and systematic guideline. Georg Thieme Verlag KG Stuttgart · New York.

A diagnostic algorithm to optimize data collection and interpretation of Ripple Maps in atrial tachycardias.

PubMed

Koa-Wing, Michael; Nakagawa, Hiroshi; Luther, Vishal; Jamil-Copley, Shahnaz; Linton, Nick; Sandler, Belinda; Qureshi, Norman; Peters, Nicholas S; Davies, D Wyn; Francis, Darrel P; Jackman, Warren; Kanagaratnam, Prapa

2015-11-15

Ripple Mapping (RM) is designed to overcome the limitations of existing isochronal 3D mapping systems by representing the intracardiac electrogram as a dynamic bar on a surface bipolar voltage map that changes in height according to the electrogram voltage-time relationship, relative to a fiduciary point. We tested the hypothesis that standard approaches to atrial tachycardia CARTO™ activation maps were inadequate for RM creation and interpretation. From the results, we aimed to develop an algorithm to optimize RMs for future prospective testing on a clinical RM platform. CARTO-XP™ activation maps from atrial tachycardia ablations were reviewed by two blinded assessors on an off-line RM workstation. Ripple Maps were graded according to a diagnostic confidence scale (Grade I - high confidence with clear pattern of activation through to Grade IV - non-diagnostic). The RM-based diagnoses were corroborated against the clinical diagnoses. 43 RMs from 14 patients were classified as Grade I (5 [11.5%]); Grade II (17 [39.5%]); Grade III (9 [21%]) and Grade IV (12 [28%]). Causes of low gradings/errors included the following: insufficient chamber point density; window-of-interest<100% of cycle length (CL); <95% tachycardia CL mapped; variability of CL and/or unstable fiducial reference marker; and suboptimal bar height and scar settings. A data collection and map interpretation algorithm has been developed to optimize Ripple Maps in atrial tachycardias. This algorithm requires prospective testing on a real-time clinical platform. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.

PubMed

Lee, Jae-Hong; Kim, Do-Hyung; Jeong, Seong-Nyum; Choi, Seong-Ho

2018-04-01

The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

An algorithm based on OmniView technology to reconstruct sagittal and coronal planes of the fetal brain from volume datasets acquired by three-dimensional ultrasound.

PubMed

Rizzo, G; Capponi, A; Pietrolucci, M E; Capece, A; Aiello, E; Mammarella, S; Arduini, D

2011-08-01

To describe a novel algorithm, based on the new display technology 'OmniView', developed to visualize diagnostic sagittal and coronal planes of the fetal brain from volumes obtained by three-dimensional (3D) ultrasonography. We developed an algorithm to image standard neurosonographic planes by drawing dissecting lines through the axial transventricular view of 3D volume datasets acquired transabdominally. The algorithm was tested on 106 normal fetuses at 18-24 weeks of gestation and the visualization rates of brain diagnostic planes were evaluated by two independent reviewers. The algorithm was also applied to nine cases with proven brain defects. The two reviewers, using the algorithm on normal fetuses, found satisfactory images with visualization rates ranging between 71.7% and 96.2% for sagittal planes and between 76.4% and 90.6% for coronal planes. The agreement rate between the two reviewers, as expressed by Cohen's kappa coefficient, was > 0.93 for sagittal planes and > 0.89 for coronal planes. All nine abnormal volumes were identified by a single observer from among a series including normal brains, and eight of these nine cases were diagnosed correctly. This novel algorithm can be used to visualize standard sagittal and coronal planes in the fetal brain. This approach may simplify the examination of the fetal brain and reduce dependency of success on operator skill. Copyright © 2011 ISUOG. Published by John Wiley & Sons, Ltd.

Rapid diagnostic tests versus clinical diagnosis for managing people with fever in malaria endemic settings.

PubMed

Odaga, John; Sinclair, David; Lokong, Joseph A; Donegan, Sarah; Hopkins, Heidi; Garner, Paul

2014-04-17

In 2010, the World Health Organization recommended that all patients with suspected malaria are tested for malaria before treatment. In rural African settings light microscopy is often unavailable. Diagnosis has relied on detecting fever, and most people were given antimalarial drugs presumptively. Rapid diagnostic tests (RDTs) provide a point-of-care test that may improve management, particularly of people for whom the RDT excludes the diagnosis of malaria. To evaluate whether introducing RDTs into algorithms for diagnosing and treating people with fever improves health outcomes, reduces antimalarial prescribing, and is safe, compared to algorithms using clinical diagnosis. We searched the Cochrane Infectious Disease Group Specialized Register; CENTRAL (The Cochrane Library); MEDLINE; EMBASE; CINAHL; LILACS; and the metaRegister of Controlled Trials for eligible trials up to 10 January 2014. We contacted researchers in the field and reviewed the reference lists of all included trials to identify any additional trials. Individual or cluster randomized trials (RCTs) comparing RDT-supported algorithms and algorithms using clinical diagnosis alone for diagnosing and treating people with fever living in malaria-endemic settings. Two authors independently applied the inclusion criteria and extracted data. We combined data from individually and cluster RCTs using the generic inverse variance method. We presented all outcomes as risk ratios (RR) with 95% confidence intervals (CIs), and assessed the quality of evidence using the GRADE approach. We included seven trials, enrolling 17,505 people with fever or reported history of fever in this review; two individually randomized trials and five cluster randomized trials. All trials were conducted in rural African settings.In most trials the health workers diagnosing and treating malaria were nurses or clinical officers with less than one week of training in RDT supported diagnosis. Health worker prescribing adherence to RDT results was highly variable: the number of participants with a negative RDT result who received antimalarials ranged from 0% to 81%.Overall, RDT supported diagnosis had little or no effect on the number of participants remaining unwell at four to seven days after treatment (6990 participants, five trials, low quality evidence); but using RDTs reduced prescribing of antimalarials by up to three-quarters (17,287 participants, seven trials, moderate quality evidence). As would be expected, the reduction in antimalarial prescriptions was highest where health workers adherence to the RDT result was high, and where the true prevalence of malaria was lower.Using RDTs to support diagnosis did not have a consistent effect on the prescription of antibiotics, with some trials showing higher antibiotic prescribing and some showing lower prescribing in the RDT group (13,573 participants, five trials, very low quality evidence).One trial reported malaria microscopy on all enrolled patients in an area of moderate endemicity, so we could compare the number of patients in the RDT and clinical diagnosis groups that actually had microscopy confirmed malaria infection but did not receive antimalarials. No difference was detected between the two diagnostic strategies (1280 participants, one trial, low quality evidence). Algorithms incorporating RDTs can substantially reduce antimalarial prescribing if health workers adhere to the test results. Introducing RDTs has not been shown to improve health outcomes for patients, but adherence to the test result does not seem to result in worse clinical outcomes than presumptive treatment.Concentrating on improving the care of RDT negative patients could improve health outcomes in febrile children.

Comparative analysis of methods for extracting vessel network on breast MRI images

NASA Astrophysics Data System (ADS)

Gaizer, Bence T.; Vassiou, Katerina G.; Lavdas, Eleftherios; Arvanitis, Dimitrios L.; Fezoulidis, Ioannis V.; Glotsos, Dimitris T.

2017-11-01

Digital processing of MRI images aims to provide an automatized diagnostic evaluation of regular health screenings. Cancerous lesions are proven to cause an alteration in the vessel structure of the diseased organ. Currently there are several methods used for extraction of the vessel network in order to quantify its properties. In this work MRI images (Signa HDx 3.0T, GE Healthcare, courtesy of University Hospital of Larissa) of 30 female breasts were subjected to three different vessel extraction algorithms to determine the location of their vascular network. The first method is an experiment to build a graph over known points of the vessel network; the second algorithm aims to determine the direction and diameter of vessels at these points; the third approach is a seed growing algorithm, spreading selection to neighbors of the known vessel pixels. The possibilities shown by the different methods were analyzed, and quantitative measurements were performed. The data provided by these measurements showed no clear correlation with the presence or malignancy of tumors, based on the radiological diagnosis of skilled physicians.

Discrimination of premalignant lesions and cancer tissues from normal gastric tissues using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Luo, Shuwen; Chen, Changshui; Mao, Hua; Jin, Shaoqin

2013-06-01

The feasibility of early detection of gastric cancer using near-infrared (NIR) Raman spectroscopy (RS) by distinguishing premalignant lesions (adenomatous polyp, n=27) and cancer tissues (adenocarcinoma, n=33) from normal gastric tissues (n=45) is evaluated. Significant differences in Raman spectra are observed among the normal, adenomatous polyp, and adenocarcinoma gastric tissues at 936, 1003, 1032, 1174, 1208, 1323, 1335, 1450, and 1655 cm-1. Diverse statistical methods are employed to develop effective diagnostic algorithms for classifying the Raman spectra of different types of ex vivo gastric tissues, including principal component analysis (PCA), linear discriminant analysis (LDA), and naive Bayesian classifier (NBC) techniques. Compared with PCA-LDA algorithms, PCA-NBC techniques together with leave-one-out, cross-validation method provide better discriminative results of normal, adenomatous polyp, and adenocarcinoma gastric tissues, resulting in superior sensitivities of 96.3%, 96.9%, and 96.9%, and specificities of 93%, 100%, and 95.2%, respectively. Therefore, NIR RS associated with multivariate statistical algorithms has the potential for early diagnosis of gastric premalignant lesions and cancer tissues in molecular level.

Radiation dose reduction for CT lung cancer screening using ASIR and MBIR: a phantom study.

PubMed

Mathieu, Kelsey B; Ai, Hua; Fox, Patricia S; Godoy, Myrna Cobos Barco; Munden, Reginald F; de Groot, Patricia M; Pan, Tinsu

2014-03-06

The purpose of this study was to reduce the radiation dosage associated with computed tomography (CT) lung cancer screening while maintaining overall diagnostic image quality and definition of ground-glass opacities (GGOs). A lung screening phantom and a multipurpose chest phantom were used to quantitatively assess the performance of two iterative image reconstruction algorithms (adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR)) used in conjunction with reduced tube currents relative to a standard clinical lung cancer screening protocol (51 effective mAs (3.9 mGy) and filtered back-projection (FBP) reconstruction). To further assess the algorithms' performances, qualitative image analysis was conducted (in the form of a reader study) using the multipurpose chest phantom, which was implanted with GGOs of two densities. Our quantitative image analysis indicated that tube current, and thus radiation dose, could be reduced by 40% or 80% from ASIR or MBIR, respectively, compared with conventional FBP, while maintaining similar image noise magnitude and contrast-to-noise ratio. The qualitative portion of our study, which assessed reader preference, yielded similar results, indicating that dose could be reduced by 60% (to 20 effective mAs (1.6 mGy)) with either ASIR or MBIR, while maintaining GGO definition. Additionally, the readers' preferences (as indicated by their ratings) regarding overall image quality were equal or better (for a given dose) when using ASIR or MBIR, compared with FBP. In conclusion, combining ASIR or MBIR with reduced tube current may allow for lower doses while maintaining overall diagnostic image quality, as well as GGO definition, during CT lung cancer screening.

Image quality of iterative reconstruction in cranial CT imaging: comparison of model-based iterative reconstruction (MBIR) and adaptive statistical iterative reconstruction (ASiR).

PubMed

Notohamiprodjo, S; Deak, Z; Meurer, F; Maertz, F; Mueck, F G; Geyer, L L; Wirth, S

2015-01-01

The purpose of this study was to compare cranial CT (CCT) image quality (IQ) of the MBIR algorithm with standard iterative reconstruction (ASiR). In this institutional review board (IRB)-approved study, raw data sets of 100 unenhanced CCT examinations (120 kV, 50-260 mAs, 20 mm collimation, 0.984 pitch) were reconstructed with both ASiR and MBIR. Signal-to-noise (SNR) and contrast-to-noise (CNR) were calculated from attenuation values measured in caudate nucleus, frontal white matter, anterior ventricle horn, fourth ventricle, and pons. Two radiologists, who were blinded to the reconstruction algorithms, evaluated anonymized multiplanar reformations of 2.5 mm with respect to depiction of different parenchymal structures and impact of artefacts on IQ with a five-point scale (0: unacceptable, 1: less than average, 2: average, 3: above average, 4: excellent). MBIR decreased artefacts more effectively than ASiR (p < 0.01). The median depiction score for MBIR was 3, whereas the median value for ASiR was 2 (p < 0.01). SNR and CNR were significantly higher in MBIR than ASiR (p < 0.01). MBIR showed significant improvement of IQ parameters compared to ASiR. As CCT is an examination that is frequently required, the use of MBIR may allow for substantial reduction of radiation exposure caused by medical diagnostics. • Model-Based iterative reconstruction (MBIR) effectively decreased artefacts in cranial CT. • MBIR reconstructed images were rated with significantly higher scores for image quality. • Model-Based iterative reconstruction may allow reduced-dose diagnostic examination protocols.

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods

PubMed Central

Hancock, Matthew C.; Magnan, Jerry F.

2016-01-01

Abstract. In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists’ annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (±1.14)%, which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (±0.012), which increases to 0.949 (±0.007) when diameter and volume features are included and has an accuracy of 88.08 (±1.11)%. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification. PMID:27990453

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

PubMed

Hancock, Matthew C; Magnan, Jerry F

2016-10-01

In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 [Formula: see text], which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 ([Formula: see text]), which increases to 0.949 ([Formula: see text]) when diameter and volume features are included and has an accuracy of 88.08 [Formula: see text]. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

Diagnostic accuracy of 256-row multidetector CT coronary angiography with prospective ECG-gating combined with fourth-generation iterative reconstruction algorithm in the assessment of coronary artery bypass: evaluation of dose reduction and image quality.

PubMed

Ippolito, Davide; Fior, Davide; Franzesi, Cammillo Talei; Riva, Luca; Casiraghi, Alessandra; Sironi, Sandro

2017-12-01

Effective radiation dose in coronary CT angiography (CTCA) for coronary artery bypass graft (CABG) evaluation is remarkably high because of long scan lengths. Prospective electrocardiographic gating with iterative reconstruction can reduce effective radiation dose. To evaluate the diagnostic performance of low-kV CT angiography protocol with prospective ecg-gating technique and iterative reconstruction (IR) algorithm in follow-up of CABG patients compared with standard retrospective protocol. Seventy-four non-obese patients with known coronary disease treated with artery bypass grafting were prospectively enrolled. All the patients underwent 256 MDCT (Brilliance iCT, Philips) CTCA using low-dose protocol (100 kV; 800 mAs; rotation time: 0.275 s) combined with prospective ECG-triggering acquisition and fourth-generation IR technique (iDose 4 ; Philips); all the lengths of the bypass graft were included in the evaluation. A control group of 42 similar patients was evaluated with a standard retrospective ECG-gated CTCA (100 kV; 800 mAs).On both CT examinations, ROIs were placed to calculate standard deviation of pixel values and intra-vessel density. Diagnostic quality was also evaluated using a 4-point quality scale. Despite the statistically significant reduction of radiation dose evaluated with DLP (study group mean DLP: 274 mGy cm; control group mean DLP: 1224 mGy cm; P value < 0.001). No statistical differences were found between PGA group and RGH group regarding intra-vessel density absolute values and SNR. Qualitative analysis, evaluated by two radiologists in "double blind", did not reveal any significant difference in diagnostic quality of the two groups. The development of high-speed MDCT scans combined with modern IR allows an accurate evaluation of CABG with prospective ECG-gating protocols in a single breath hold, obtaining a significant reduction in radiation dose.

Sensor Fusion, Prognostics, Diagnostics and Failure Mode Control for Complex Aerospace Systems

DTIC Science & Technology

2010-10-01

algorithm   and   to   then   tune   the   candidates   individually   using   known   metaheuristics .  As  will  be...parallel. The result of this arrangement is that the processing is a form that is analogous to standard parallel genetic algorithms , and as such...search algorithm then uses the hybrid of fitness data to rank the results. The ETRAS controller is developed using pre-selection, showing that a

Should learners reason one step at a time? A randomised trial of two diagnostic scheme designs.

PubMed

Blissett, Sarah; Morrison, Deric; McCarty, David; Sibbald, Matthew

2017-04-01

Making a diagnosis can be difficult for learners as they must integrate multiple clinical variables. Diagnostic schemes can help learners with this complex task. A diagnostic scheme is an algorithm that organises possible diagnoses by assigning signs or symptoms (e.g. systolic murmur) to groups of similar diagnoses (e.g. aortic stenosis and aortic sclerosis) and provides distinguishing features to help discriminate between similar diagnoses (e.g. carotid pulse). The current literature does not identify whether scheme layouts should guide learners to reason one step at a time in a terminally branching scheme or weigh multiple variables simultaneously in a hybrid scheme. We compared diagnostic accuracy, perceptual errors and cognitive load using two scheme layouts for cardiac auscultation. Focused on the task of identifying murmurs on Harvey, a cardiopulmonary simulator, 86 internal medicine residents used two scheme layouts. The terminally branching scheme organised the information into single variable decisions. The hybrid scheme combined single variable decisions with a chart integrating multiple distinguishing features. Using a crossover design, participants completed one set of murmurs (diastolic or systolic) with either the terminally branching or the hybrid scheme. The second set of murmurs was completed with the other scheme. A repeated measures manova was performed to compare diagnostic accuracy, perceptual errors and cognitive load between the scheme layouts. There was a main effect of the scheme layout (Wilks' λ = 0.841, F 3,80 = 5.1, p = 0.003). Use of a terminally branching scheme was associated with increased diagnostic accuracy (65 versus 53%, p = 0.02), fewer perceptual errors (0.61 versus 0.98 errors, p = 0.001) and lower cognitive load (3.1 versus 3.5/7, p = 0.023). The terminally branching scheme was associated with improved diagnostic accuracy, fewer perceptual errors and lower cognitive load, suggesting that terminally branching schemes are effective for improving diagnostic accuracy. These findings can inform the design of schemes and other clinical decision aids. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Generalized pixel profiling and comparative segmentation with application to arteriovenous malformation segmentation.

PubMed

Babin, D; Pižurica, A; Bellens, R; De Bock, J; Shang, Y; Goossens, B; Vansteenkiste, E; Philips, W

2012-07-01

Extraction of structural and geometric information from 3-D images of blood vessels is a well known and widely addressed segmentation problem. The segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, with a special application in diagnostics and surgery on arteriovenous malformations (AVM). However, the techniques addressing the problem of the AVM inner structure segmentation are rare. In this work we present a novel method of pixel profiling with the application to segmentation of the 3-D angiography AVM images. Our algorithm stands out in situations with low resolution images and high variability of pixel intensity. Another advantage of our method is that the parameters are set automatically, which yields little manual user intervention. The results on phantoms and real data demonstrate its effectiveness and potentials for fine delineation of AVM structure. Copyright © 2012 Elsevier B.V. All rights reserved.

A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients.

PubMed

Sideris, Costas; Alshurafa, Nabil; Pourhomayoun, Mohammad; Shahmohammadi, Farhad; Samy, Lauren; Sarrafzadeh, Majid

2015-01-01

In this paper, we propose a novel methodology for utilizing disease diagnostic information to predict severity of condition for Congestive Heart Failure (CHF) patients. Our methodology relies on a novel, clustering-based, feature extraction framework using disease diagnostic information. To reduce the dimensionality we identify disease clusters using cooccurence frequencies. We then utilize these clusters as features to predict patient severity of condition. We build our clustering and feature extraction algorithm using the 2012 National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP) which contains 7 million discharge records and ICD-9-CM codes. The proposed framework is tested on Ronald Reagan UCLA Medical Center Electronic Health Records (EHR) from 3041 patients. We compare our cluster-based feature set with another that incorporates the Charlson comorbidity score as a feature and demonstrate an accuracy improvement of up to 14% in the predictability of the severity of condition.

Laser Blow-Off Impurity Injection Experiments at the HSX Stellarator

NASA Astrophysics Data System (ADS)

Castillo, J. F.; Bader, A.; Likin, K. M.; Anderson, D. T.; Anderson, F. S. B.; Kumar, S. T. A.; Talmadge, J. N.

2017-10-01

Results from the HSX laser blow-off experiment are presented and compared to a synthetic diagnostic implemented in the STRAHL impurity transport modeling code in order to measure the impurity transport diffusivity and convective velocity. A laser blow-off impurity injection system is used to rapidly deposit a small, controlled quantity of aluminum into the confinement volume. Five AXUV photodiode arrays are used to take time-resolved measurements of the impurity radiation. The spatially one-dimensional impurity transport code STRAHL is used to calculate a time-dependent plasma emissivity profile. Modeled intensity signals calculated from a synthetic diagnostic code provide direct comparison between plasma simulation and experimental results. An optimization algorithm with impurity transport coefficients acting as free parameters is used to fit the model to experimental data. This work is supported by US DOE Grant DE-FG02-93ER54222.

Accuracy of vaginal symptom self-diagnosis algorithms for deployed military women.

PubMed

Ryan-Wenger, Nancy A; Neal, Jeremy L; Jones, Ashley S; Lowe, Nancy K

2010-01-01

Deployed military women have an increased risk for development of vaginitis due to extreme temperatures, primitive sanitation, hygiene and laundry facilities, and unavailable or unacceptable healthcare resources. The Women in the Military Self-Diagnosis (WMSD) and treatment kit was developed as a field-expedient solution to this problem. The primary study aims were to evaluate the accuracy of women's self-diagnosis of vaginal symptoms and eight diagnostic algorithms and to predict potential self-medication omission and commission error rates. Participants included 546 active duty, deployable Army (43.3%) and Navy (53.6%) women with vaginal symptoms who sought healthcare at troop medical clinics on base.In the clinic lavatory, women conducted a self-diagnosis using a sterile cotton swab to obtain vaginal fluid, a FemExam card to measure positive or negative pH and amines, and the investigator-developed WMSD Decision-Making Guide. Potential self-diagnoses were "bacterial infection" (bacterial vaginosis [BV] and/or trichomonas vaginitis [TV]), "yeast infection" (candida vaginitis [CV]), "no infection/normal," or "unclear." The Affirm VPIII laboratory reference standard was used to detect clinically significant amounts of vaginal fluid DNA for organisms associated with BV, TV, and CV. Women's self-diagnostic accuracy was 56% for BV/TV and 69.2% for CV. False-positives would have led to a self-medication commission error rate of 20.3% for BV/TV and 8% for CV. Potential self-medication omission error rates due to false-negatives were 23.7% for BV/TV and 24.8% for CV. The positive predictive value of diagnostic algorithms ranged from 0% to 78.1% for BV/TV and 41.7% for CV. The algorithms were based on clinical diagnostic standards. The nonspecific nature of vaginal symptoms, mixed infections, and a faulty device intended to measure vaginal pH and amines explain why none of the algorithms reached the goal of 95% accuracy. The next prototype of the WMSD kit will not include nonspecific vaginal signs and symptoms in favor of recently available point-of-care devices that identify antigens or enzymes of the causative BV, TV, and CV organisms.

[Diagnostic algorithm in chronic myeloproliferative diseases (CMPD)].

PubMed

Haferlach, Torsten; Bacher, Ulrike; Kern, Wolfgang; Schnittger, Susanne; Haferlach, Claudia

2007-09-15

The Philadelphia-negative chronic myeloproliferative diseases (CMPD) are very complex and heterogeneous disorders. They are represented by polycythemia vera (PV), chronic idiopathic myelofibrosis (CIMF), essential thrombocythemia (ET), CMPD/unclassifiable (CMPD-U), chronic neutrophilic leukemia (CNL), and chronic eosinophilic leukemia/hypereosinophilic syndrome (CEL/HES) according to the WHO classification. Before, diagnostics were mainly focused on clinical and morphological aspects, but in recent years cytogenetics and fluorescence in situ hybridization (FISH) found entrance in routine schedules as chromosomal abnormalities are relevant for prognosis and classification. Recently, there is rapid progress in the field of molecular characterization: the JAK2V617F mutation which shows a high incidence in PV, CIMF, and ET already plays a central role and will probably soon be included in follow-up procedures. Due to the detection of mutations in exon 12 of the JAK2 gene or mutations in the MPL gene the variety of activating mutations in the CMPD is still increasing. In CEL/HES the detection of the FIP1L1-PDGFRA fusion gene and overexpression of PDGFRA and PDGFRB led to targeted therapy with tyrosine kinase inhibitors. Thus, diagnostics in the CMPD transform toward a multimodal diagnostic concept based on a combination of methods - cyto-/histomorphology, cytogenetics, and individual molecular methods which can be included in a diagnostic algorithm.

Can administrative health utilisation data provide an accurate diabetes prevalence estimate for a geographical region?

PubMed

Chan, Wing Cheuk; Papaconstantinou, Dean; Lee, Mildred; Telfer, Kendra; Jo, Emmanuel; Drury, Paul L; Tobias, Martin

2018-05-01

To validate the New Zealand Ministry of Health (MoH) Virtual Diabetes Register (VDR) using longitudinal laboratory results and to develop an improved algorithm for estimating diabetes prevalence at a population level. The assigned diabetes status of individuals based on the 2014 version of the MoH VDR is compared to the diabetes status based on the laboratory results stored in the Auckland regional laboratory result repository (TestSafe) using the New Zealand diabetes diagnostic criteria. The existing VDR algorithm is refined by reviewing the sensitivity and positive predictive value of the each of the VDR algorithm rules individually and as a combination. The diabetes prevalence estimate based on the original 2014 MoH VDR was 17% higher (n = 108,505) than the corresponding TestSafe prevalence estimate (n = 92,707). Compared to the diabetes prevalence based on TestSafe, the original VDR has a sensitivity of 89%, specificity of 96%, positive predictive value of 76% and negative predictive value of 98%. The modified VDR algorithm has improved the positive predictive value by 6.1% and the specificity by 1.4% with modest reductions in sensitivity of 2.2% and negative predictive value of 0.3%. At an aggregated level the overall diabetes prevalence estimated by the modified VDR is 5.7% higher than the corresponding estimate based on TestSafe. The Ministry of Health Virtual Diabetes Register algorithm has been refined to provide a more accurate diabetes prevalence estimate at a population level. The comparison highlights the potential value of a national population long term condition register constructed from both laboratory results and administrative data. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimizing Tissue Sampling for the Diagnosis, Subtyping, and Molecular Analysis of Lung Cancer

PubMed Central

Ofiara, Linda Marie; Navasakulpong, Asma; Beaudoin, Stephane; Gonzalez, Anne Valerie

2014-01-01

Lung cancer has entered the era of personalized therapy with histologic subclassification and the presence of molecular biomarkers becoming increasingly important in therapeutic algorithms. At the same time, biopsy specimens are becoming increasingly smaller as diagnostic algorithms seek to establish diagnosis and stage with the least invasive techniques. Here, we review techniques used in the diagnosis of lung cancer including bronchoscopy, ultrasound-guided bronchoscopy, transthoracic needle biopsy, and thoracoscopy. In addition to discussing indications and complications, we focus our discussion on diagnostic yields and the feasibility of testing for molecular biomarkers such as epidermal growth factor receptor and anaplastic lymphoma kinase, emphasizing the importance of a sufficient tumor biopsy. PMID:25295226

Multispectral autofluorescence diagnosis of non-melanoma cutaneous tumors

NASA Astrophysics Data System (ADS)

Borisova, Ekaterina; Dogandjiiska, Daniela; Bliznakova, Irina; Avramov, Latchezar; Pavlova, Elmira; Troyanova, Petranka

2009-07-01

Fluorescent analysis of basal cell carcinoma (BCC), squamous cell carcinoma (SCC), keratoacanthoma and benign cutaneous lesions is carried out under initial phase of clinical trial in the National Oncological Center - Sofia. Excitation sources with maximum of emission at 365, 380, 405, 450 and 630 nm are applied for better differentiation between nonmelanoma malignant cutaneous lesions fluorescence and spectral discrimination from the benign pathologies. Major spectral features are addressed and diagnostic discrimination algorithms based on lesions' emission properties are proposed. The diagnostic algorithms and evaluation procedures found will be applied for development of an optical biopsy clinical system for skin cancer detection in the frames of National Oncological Center and other university hospital dermatological departments in our country.

[Coagulation Monitoring and Bleeding Management in Cardiac Surgery].

PubMed

Bein, Berthold; Schiewe, Robert

2018-05-01

The transfusion of allogeneic blood products is associated with increased morbidity and mortality. An impaired hemostasis is frequently found in patients undergoing cardiac surgery and may in turn cause bleeding and transfusions. A goal directed coagulation management addressing the often complex coagulation disorders needs sophisticated diagnostics. This may improve both patients' outcome and costs. Recent data suggest that coagulation management based on a rational algorithm is more effective than traditional therapy based on conventional laboratory variables such as PT and INR. Platelet inhibitors, cumarins, direct oral anticoagulants and heparin need different diagnostic and therapeutic approaches. An algorithm specifically developed for use during cardiac surgery is presented. Georg Thieme Verlag KG Stuttgart · New York.

Algorithms imaging tests comparison following the first febrile urinary tract infection in children.

PubMed

Tombesi, María M; Alconcher, Laura F; Lucarelli, Lucas; Ciccioli, Agustina

2017-08-01

To compare the diagnostic sensitivity, costs and radiation doses of imaging tests algorithms developed by the Argentine Society of Pediatrics in 2003 and 2015, against British and American guidelines after the first febrile urinary tract infection (UTI). Inclusion criteria: children ≤ 2 years old with their first febrile UTI and normal ultrasound, voiding cystourethrography and dimercaptosuccinic acid scintigraphy, according to the algorithm established by the Argentine Society of Pediatrics in 2003, treated between 2003 and 2010. The comparisons between algorithms were carried out through retrospective simulation. Eighty (80) patients met the inclusion criteria; 51 (63%) had vesicoureteral reflux (VUR); 6% of the cases were severe. Renal scarring was observed in 6 patients (7.5%). Cost: ARS 404,000. Radiation: 160 millisieverts. With the Argentine Society of Pediatrics' algorithm developed in 2015, the diagnosis of 4 VURs and 2 cases of renal scarring would have been missed. The cost of this omission would have been ARS 301,800 and 124 millisieverts of radiation. British and American guidelines would have missed the diagnosis of all VURs and all cases of renal scarring, with a related cost of ARS 23,000 and ARS 40,000, respectively and 0 radiation. Intensive protocols are highly sensitive to VUR and renal scarring, but they imply high costs and doses of radiation, and result in questionable benefits. Sociedad Argentina de Pediatría

High-grade video compression of echocardiographic studies: a multicenter validation study of selected motion pictures expert groups (MPEG)-4 algorithms.

PubMed

Barbier, Paolo; Alimento, Marina; Berna, Giovanni; Celeste, Fabrizio; Gentile, Francesco; Mantero, Antonio; Montericcio, Vincenzo; Muratori, Manuela

2007-05-01

Large files produced by standard compression algorithms slow down spread of digital and tele-echocardiography. We validated echocardiographic video high-grade compression with the new Motion Pictures Expert Groups (MPEG)-4 algorithms with a multicenter study. Seven expert cardiologists blindly scored (5-point scale) 165 uncompressed and compressed 2-dimensional and color Doppler video clips, based on combined diagnostic content and image quality (uncompressed files as references). One digital video and 3 MPEG-4 algorithms (WM9, MV2, and DivX) were used, the latter at 3 compression levels (0%, 35%, and 60%). Compressed file sizes decreased from 12 to 83 MB to 0.03 to 2.3 MB (1:1051-1:26 reduction ratios). Mean SD of differences was 0.81 for intraobserver variability (uncompressed and digital video files). Compared with uncompressed files, only the DivX mean score at 35% (P = .04) and 60% (P = .001) compression was significantly reduced. At subcategory analysis, these differences were still significant for gray-scale and fundamental imaging but not for color or second harmonic tissue imaging. Original image quality, session sequence, compression grade, and bitrate were all independent determinants of mean score. Our study supports use of MPEG-4 algorithms to greatly reduce echocardiographic file sizes, thus facilitating archiving and transmission. Quality evaluation studies should account for the many independent variables that affect image quality grading.

A biological phantom for evaluation of CT image reconstruction algorithms

NASA Astrophysics Data System (ADS)

Cammin, J.; Fung, G. S. K.; Fishman, E. K.; Siewerdsen, J. H.; Stayman, J. W.; Taguchi, K.

2014-03-01

In recent years, iterative algorithms have become popular in diagnostic CT imaging to reduce noise or radiation dose to the patient. The non-linear nature of these algorithms leads to non-linearities in the imaging chain. However, the methods to assess the performance of CT imaging systems were developed assuming the linear process of filtered backprojection (FBP). Those methods may not be suitable any longer when applied to non-linear systems. In order to evaluate the imaging performance, a phantom is typically scanned and the image quality is measured using various indices. For reasons of practicality, cost, and durability, those phantoms often consist of simple water containers with uniform cylinder inserts. However, these phantoms do not represent the rich structure and patterns of real tissue accurately. As a result, the measured image quality or detectability performance for lesions may not reflect the performance on clinical images. The discrepancy between estimated and real performance may be even larger for iterative methods which sometimes produce "plastic-like", patchy images with homogeneous patterns. Consequently, more realistic phantoms should be used to assess the performance of iterative algorithms. We designed and constructed a biological phantom consisting of porcine organs and tissue that models a human abdomen, including liver lesions. We scanned the phantom on a clinical CT scanner and compared basic image quality indices between filtered backprojection and an iterative reconstruction algorithm.

Clinical Evaluation of 68Ga-PSMA-II and 68Ga-RM2 PET Images Reconstructed With an Improved Scatter Correction Algorithm.

PubMed

Wangerin, Kristen A; Baratto, Lucia; Khalighi, Mohammad Mehdi; Hope, Thomas A; Gulaka, Praveen K; Deller, Timothy W; Iagaru, Andrei H

2018-06-06

Gallium-68-labeled radiopharmaceuticals pose a challenge for scatter estimation because their targeted nature can produce high contrast in these regions of the kidneys and bladder. Even small errors in the scatter estimate can result in washout artifacts. Administration of diuretics can reduce these artifacts, but they may result in adverse events. Here, we investigated the ability of algorithmic modifications to mitigate washout artifacts and eliminate the need for diuretics or other interventions. The model-based scatter algorithm was modified to account for PET/MRI scanner geometry and challenges of non-FDG tracers. Fifty-three clinical 68 Ga-RM2 and 68 Ga-PSMA-11 whole-body images were reconstructed using the baseline scatter algorithm. For comparison, reconstruction was also processed with modified sampling in the single-scatter estimation and with an offset in the scatter tail-scaling process. None of the patients received furosemide to attempt to decrease the accumulation of radiopharmaceuticals in the bladder. The images were scored independently by three blinded reviewers using the 5-point Likert scale. The scatter algorithm improvements significantly decreased or completely eliminated the washout artifacts. When comparing the baseline and most improved algorithm, the image quality increased and image artifacts were reduced for both 68 Ga-RM2 and for 68 Ga-PSMA-11 in the kidneys and bladder regions. Image reconstruction with the improved scatter correction algorithm mitigated washout artifacts and recovered diagnostic image quality in 68 Ga PET, indicating that the use of diuretics may be avoided.

Mutual Information Item Selection Method in Cognitive Diagnostic Computerized Adaptive Testing with Short Test Length

ERIC Educational Resources Information Center

Wang, Chun

2013-01-01

Cognitive diagnostic computerized adaptive testing (CD-CAT) purports to combine the strengths of both CAT and cognitive diagnosis. Cognitive diagnosis models aim at classifying examinees into the correct mastery profile group so as to pinpoint the strengths and weakness of each examinee whereas CAT algorithms choose items to determine those…

Four-dimensional wavelet compression of arbitrarily sized echocardiographic data.

PubMed

Zeng, Li; Jansen, Christian P; Marsch, Stephan; Unser, Michael; Hunziker, Patrick R

2002-09-01

Wavelet-based methods have become most popular for the compression of two-dimensional medical images and sequences. The standard implementations consider data sizes that are powers of two. There is also a large body of literature treating issues such as the choice of the "optimal" wavelets and the performance comparison of competing algorithms. With the advent of telemedicine, there is a strong incentive to extend these techniques to higher dimensional data such as dynamic three-dimensional (3-D) echocardiography [four-dimensional (4-D) datasets]. One of the practical difficulties is that the size of this data is often not a multiple of a power of two, which can lead to increased computational complexity and impaired compression power. Our contribution in this paper is to present a genuine 4-D extension of the well-known zerotree algorithm for arbitrarily sized data. The key component of our method is a one-dimensional wavelet algorithm that can handle arbitrarily sized input signals. The method uses a pair of symmetric/antisymmetric wavelets (10/6) together with some appropriate midpoint symmetry boundary conditions that reduce border artifacts. The zerotree structure is also adapted so that it can accommodate noneven data splitting. We have applied our method to the compression of real 3-D dynamic sequences from clinical cardiac ultrasound examinations. Our new algorithm compares very favorably with other more ad hoc adaptations (image extension and tiling) of the standard powers-of-two methods, in terms of both compression performance and computational cost. It is vastly superior to slice-by-slice wavelet encoding. This was seen not only in numerical image quality parameters but also in expert ratings, where significant improvement using the new approach could be documented. Our validation experiments show that one can safely compress 4-D data sets at ratios of 128:1 without compromising the diagnostic value of the images. We also display some more extreme compression results at ratios of 2000:1 where some key diagnostically relevant key features are preserved.

Doppler distortion correction based on microphone array and matching pursuit algorithm for a wayside train bearing monitoring system

NASA Astrophysics Data System (ADS)

Liu, Xingchen; Hu, Zhiyong; He, Qingbo; Zhang, Shangbin; Zhu, Jun

2017-10-01

Doppler distortion and background noise can reduce the effectiveness of wayside acoustic train bearing monitoring and fault diagnosis. This paper proposes a method of combining a microphone array and matching pursuit algorithm to overcome these difficulties. First, a dictionary is constructed based on the characteristics and mechanism of a far-field assumption. Then, the angle of arrival of the train bearing is acquired when applying matching pursuit to analyze the acoustic array signals. Finally, after obtaining the resampling time series, the Doppler distortion can be corrected, which is convenient for further diagnostic work. Compared with traditional single-microphone Doppler correction methods, the advantages of the presented array method are its robustness to background noise and its barely requiring pre-measuring parameters. Simulation and experimental study show that the proposed method is effective in performing wayside acoustic bearing fault diagnosis.

Secured remote health monitoring system

PubMed Central

Ganesh Kumar, Pugalendhi

2017-01-01

Wireless medical sensor network is used in healthcare applications that have the collections of biosensors connected to a human body or emergency care unit to monitor the patient's physiological vital status. The real-time medical data collected using wearable medical sensors are transmitted to a diagnostic centre. The data generated from the sensors are aggregated at this centre and transmitted further to the doctor's personal digital assistant for diagnosis. The unauthorised access of one's health data may lead to misuse and legal complications while unreliable data transmission or storage may lead to life threatening risk to patients. So, this Letter combines the symmetric algorithm and attribute-based encryption to secure the data transmission and access control system for medical sensor network. In this work, existing systems and their algorithm are compared for identifying the best performance. The work also shows the graphical comparison of encryption time, decryption time and total computation time of the existing and the proposed systems. PMID:29383257

Hyperspectral imaging for melanoma screening

NASA Astrophysics Data System (ADS)

Martin, Justin; Krueger, James; Gareau, Daniel

2014-03-01

The 5-year survival rate for patients diagnosed with Melanoma, a deadly form of skin cancer, in its latest stages is about 15%, compared to over 90% for early detection and treatment. We present an imaging system and algorithm that can be used to automatically generate a melanoma risk score to aid clinicians in the early identification of this form of skin cancer. Our system images the patient's skin at a series of different wavelengths and then analyzes several key dermoscopic features to generate this risk score. We have found that shorter wavelengths of light are sensitive to information in the superficial areas of the skin while longer wavelengths can be used to gather information at greater depths. This accompanying diagnostic computer algorithm has demonstrated much higher sensitivity and specificity than the currently commercialized system in preliminary trials and has the potential to improve the early detection of melanoma.

Application of optical longitudinal tomography for dental introscopy

NASA Astrophysics Data System (ADS)

Levin, Gennady G.; Burgansky, Alexander A.; Levandovski, Alexei G.

1997-08-01

A new method of dental introscopy in-vitro is suggested by the authors. This method implies the usage of longitudinal tomography techniques and is characterized by non-invasive and non-harmful diagnostics features, as well as interactive regime of image reconstruction which lets an operator (doctor) to control the diagnostics process in real time. He-Ne laser emission is used for obtaining of the projections. By the means of longitudinal tomography, images of different sections of an object (tooth) can be reconstructed. An experiment was held by the authors in which 100 projections of a tooth (premolar) were obtained and images of 10 different sections were reconstructed. These images were later compared to real sections of the tooth. This experiment proved that optical longitudinal tomography can be successfully used for dental introscopy. Authors claim that optical tomographic methods can be used for diagnostics of other biological objects as well. Such objects are characterized by spatial geometrical anisotropy (tubular bones, phalanxes of fingers, penis, etc.). It is especially promising to use this method for children's dentistry. the authors discuss some features of the data acquisition system for optical longitudinal tomography. Reconstruction algorithms are described. The results of experimental reconstruction are presented and advantages of this diagnostics method are discussed.

Evaluation of the diagnostic potential of ex vivo Raman spectroscopy in gastric cancers: fingerprint versus high wavenumber

NASA Astrophysics Data System (ADS)

Zhou, Xueqian; Dai, Jianhua; Chen, Yao; Duan, Guangjie; Liu, Yulong; Zhang, Hua; Wu, Hongbo; Peng, Guiyong

2016-10-01

The aim of this study was to apply Raman spectroscopy in the high wavenumber (HW) region (2800 to 3000 cm-1) for ex vivo detection of gastric cancer and compare its diagnostic potential with that of the fingerprint (FP) region (800 to 1800 cm-1). Raman spectra were collected in the FP and HW regions to differentiate between normal mucosa (n=38) and gastric cancer (n=37). The distinctive Raman spectral differences between normal and cancer tissues are observed at 853, 879, 1157, 1319, 1338, 1448, and 2932 cm-1 and are primarily related to proteins, lipids, nucleic acids, collagen, and carotenoids in the tissue. In FP and HW Raman spectroscopy for diagnosis of gastric cancer, multivariate diagnostic algorithms based on partial-least-squares discriminant analysis, together with leave-one-sample-out cross validation, yielded diagnostic sensitivities of 94.59% and 81.08%, and specificities of 86.84% and 71.05%, respectively. Receiver operating characteristic analysis further confirmed that the FP region model performance is superior to that of the HW region model. Better differentiation between normal and gastric cancer tissues can be achieved using FP Raman spectroscopy and PLS-DA techniques, but the complementary natures of the FP and HW regions make both of them useful in diagnosis of gastric cancer.

VLSI (Very Large Scale Integrated Circuits) Design with the MacPitts Silicon Compiler.

DTIC Science & Technology

1985-09-01

the background. If the algorithm is not fully debugged, then issue instead macpitts basename herald so MacPitts diagnostics and Liszt diagnostics both...command interpreter. Upon compilation, however, the following LI!F compiler ( Liszt ) diagnostic results, Error: Non-number to minus nil where the first...language used in the MacPitts source code. The more instructive solution is to write the Franz LISP code to decide if a jumper wire is needed, and if so, to

Suitability of the echo-time-shift method as laboratory standard for thermal ultrasound dosimetry

NASA Astrophysics Data System (ADS)

Fuhrmann, Tina; Georg, Olga; Haller, Julian; Jenderka, Klaus-Vitold

2017-03-01

Ultrasound therapy is a promising, non-invasive application with potential to significantly improve cancer therapies like surgery, viro- or immunotherapy. This therapy needs faster, cheaper and more easy-to-handle quality assurance tools for therapy devices as well as possibilities to verify treatment plans and for dosimetry. This limits comparability and safety of treatments. Accurate spatial and temporal temperature maps could be used to overcome these shortcomings. In this contribution first results of suitability and accuracy investigations of the echo-time-shift method for two-dimensional temperature mapping during and after sonication are presented. The analysis methods used to calculate time-shifts were a discrete frame-to-frame and a discrete frame-to-base-frame algorithm as well as a sigmoid fit for temperature calculation. In the future accuracy could be significantly enhanced by using continuous methods for time-shift calculation. Further improvements can be achieved by improving filtering algorithms and interpolation of sampled diagnostic ultrasound data. It might be a comparatively accurate, fast and affordable method for laboratory and clinical quality control.

Identifying injection drug use and estimating population size of people who inject drugs using healthcare administrative datasets.

PubMed

Janjua, Naveed Zafar; Islam, Nazrul; Kuo, Margot; Yu, Amanda; Wong, Stanley; Butt, Zahid A; Gilbert, Mark; Buxton, Jane; Chapinal, Nuria; Samji, Hasina; Chong, Mei; Alvarez, Maria; Wong, Jason; Tyndall, Mark W; Krajden, Mel

2018-05-01

Large linked healthcare administrative datasets could be used to monitor programs providing prevention and treatment services to people who inject drugs (PWID). However, diagnostic codes in administrative datasets do not differentiate non-injection from injection drug use (IDU). We validated algorithms based on diagnostic codes and prescription records representing IDU in administrative datasets against interview-based IDU data. The British Columbia Hepatitis Testers Cohort (BC-HTC) includes ∼1.7 million individuals tested for HCV/HIV or reported HBV/HCV/HIV/tuberculosis cases in BC from 1990 to 2015, linked to administrative datasets including physician visit, hospitalization and prescription drug records. IDU, assessed through interviews as part of enhanced surveillance at the time of HIV or HCV/HBV diagnosis from a subset of cases included in the BC-HTC (n = 6559), was used as the gold standard. ICD-9/ICD-10 codes for IDU and injecting-related infections (IRI) were grouped with records of opioid substitution therapy (OST) into multiple IDU algorithms in administrative datasets. We assessed the performance of IDU algorithms through calculation of sensitivity, specificity, positive predictive, and negative predictive values. Sensitivity was highest (90-94%), and specificity was lowest (42-73%) for algorithms based either on IDU or IRI and drug misuse codes. Algorithms requiring both drug misuse and IRI had lower sensitivity (57-60%) and higher specificity (90-92%). An optimal sensitivity and specificity combination was found with two medical visits or a single hospitalization for injectable drugs with (83%/82%) and without OST (78%/83%), respectively. Based on algorithms that included two medical visits, a single hospitalization or OST records, there were 41,358 (1.2% of 11-65 years individuals in BC) recent PWID in BC based on health encounters during 3- year period (2013-2015). Algorithms for identifying PWID using diagnostic codes in linked administrative data could be used for tracking the progress of programing aimed at PWID. With population-based datasets, this tool can be used to inform much needed estimates of PWID population size. Copyright © 2018 Elsevier B.V. All rights reserved.

[Methods of statistical analysis in differential diagnostics of the degree of brain glioma anaplasia during preoperative stage].

PubMed

Glavatskiĭ, A Ia; Guzhovskaia, N V; Lysenko, S N; Kulik, A V

2005-12-01

The authors proposed a possible preoperative diagnostics of the degree of supratentorial brain gliom anaplasia using statistical analysis methods. It relies on a complex examination of 934 patients with I-IV degree anaplasias, which had been treated in the Institute of Neurosurgery from 1990 to 2004. The use of statistical analysis methods for differential diagnostics of the degree of brain gliom anaplasia may optimize a diagnostic algorithm, increase reliability of obtained data and in some cases avoid carrying out irrational operative intrusions. Clinically important signs for the use of statistical analysis methods directed to preoperative diagnostics of brain gliom anaplasia have been defined

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

Sethi, Gaurav; Saini, B S

2015-12-01

This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.

A landslide-quake detection algorithm with STA/LTA and diagnostic functions of moving average and scintillation index: A preliminary case study of the 2009 Typhoon Morakot in Taiwan

NASA Astrophysics Data System (ADS)

Wu, Yu-Jie; Lin, Guan-Wei

2017-04-01

Since 1999, Taiwan has experienced a rapid rise in the number of landslides, and the number even reached a peak after the 2009 Typhoon Morakot. Although it is proved that the ground-motion signals induced by slope processes could be recorded by seismograph, it is difficult to be distinguished from continuous seismic records due to the lack of distinct P and S waves. In this study, we combine three common seismic detectors including the short-term average/long-term average (STA/LTA) approach, and two diagnostic functions of moving average and scintillation index. Based on these detectors, we have established an auto-detection algorithm of landslide-quakes and the detection thresholds are defined to distinguish landslide-quake from earthquakes and background noises. To further improve the proposed detection algorithm, we apply it to seismic archives recorded by Broadband Array in Taiwan for Seismology (BATS) during the 2009 Typhoon Morakots and consequently the discrete landslide-quakes detected by the automatic algorithm are located. The detection algorithm show that the landslide-detection results are consistent with that of visual inspection and hence can be used to automatically monitor landslide-quakes.

Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software.

PubMed

Moon, Andres; Smith, Geoffrey H; Kong, Jun; Rogers, Thomas E; Ellis, Carla L; Farris, Alton B Brad

2018-02-01

Renal allograft rejection diagnosis depends on assessment of parameters such as interstitial inflammation; however, studies have shown interobserver variability regarding interstitial inflammation assessment. Since automated image analysis quantitation can be reproducible, we devised customized analysis methods for CD3+ T-cell staining density as a measure of rejection severity and compared them with established commercial methods along with visual assessment. Renal biopsy CD3 immunohistochemistry slides (n = 45), including renal allografts with various degrees of acute cellular rejection (ACR) were scanned for whole slide images (WSIs). Inflammation was quantitated in the WSIs using pathologist visual assessment, commercial algorithms (Aperio nuclear algorithm for CD3+ cells/mm 2 and Aperio positive pixel count algorithm), and customized open source algorithms developed in ImageJ with thresholding/positive pixel counting (custom CD3+%) and identification of pixels fulfilling "maxima" criteria for CD3 expression (custom CD3+ cells/mm 2 ). Based on visual inspections of "markup" images, CD3 quantitation algorithms produced adequate accuracy. Additionally, CD3 quantitation algorithms correlated between each other and also with visual assessment in a statistically significant manner (r = 0.44 to 0.94, p = 0.003 to < 0.0001). Methods for assessing inflammation suggested a progression through the tubulointerstitial ACR grades, with statistically different results in borderline versus other ACR types, in all but the custom methods. Assessment of CD3-stained slides using various open source image analysis algorithms presents salient correlations with established methods of CD3 quantitation. These analysis techniques are promising and highly customizable, providing a form of on-slide "flow cytometry" that can facilitate additional diagnostic accuracy in tissue-based assessments.

Accurate derivation of heart rate variability signal for detection of sleep disordered breathing in children.

PubMed

Chatlapalli, S; Nazeran, H; Melarkod, V; Krishnam, R; Estrada, E; Pamula, Y; Cabrera, S

2004-01-01

The electrocardiogram (ECG) signal is used extensively as a low cost diagnostic tool to provide information concerning the heart's state of health. Accurate determination of the QRS complex, in particular, reliable detection of the R wave peak, is essential in computer based ECG analysis. ECG data from Physionet's Sleep-Apnea database were used to develop, test, and validate a robust heart rate variability (HRV) signal derivation algorithm. The HRV signal was derived from pre-processed ECG signals by developing an enhanced Hilbert transform (EHT) algorithm with built-in missing beat detection capability for reliable QRS detection. The performance of the EHT algorithm was then compared against that of a popular Hilbert transform-based (HT) QRS detection algorithm. Autoregressive (AR) modeling of the HRV power spectrum for both EHT- and HT-derived HRV signals was achieved and different parameters from their power spectra as well as approximate entropy were derived for comparison. Poincare plots were then used as a visualization tool to highlight the detection of the missing beats in the EHT method After validation of the EHT algorithm on ECG data from the Physionet, the algorithm was further tested and validated on a dataset obtained from children undergoing polysomnography for detection of sleep disordered breathing (SDB). Sensitive measures of accurate HRV signals were then derived to be used in detecting and diagnosing sleep disordered breathing in children. All signal processing algorithms were implemented in MATLAB. We present a description of the EHT algorithm and analyze pilot data for eight children undergoing nocturnal polysomnography. The pilot data demonstrated that the EHT method provides an accurate way of deriving the HRV signal and plays an important role in extraction of reliable measures to distinguish between periods of normal and sleep disordered breathing (SDB) in children.

Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

PubMed Central

Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Fu, Jing-Jing; Xu, Zhao-Xia; Guo, Rui; Qian, Peng

2012-01-01

Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:22719781

Real-time plasma control based on the ISTTOK tomography diagnostica)

NASA Astrophysics Data System (ADS)

Carvalho, P. J.; Carvalho, B. B.; Neto, A.; Coelho, R.; Fernandes, H.; Sousa, J.; Varandas, C.; Chávez-Alarcón, E.; Herrera-Velázquez, J. J. E.

2008-10-01

The presently available processing power in generic processing units (GPUs) combined with state-of-the-art programmable logic devices benefits the implementation of complex, real-time driven, data processing algorithms for plasma diagnostics. A tomographic reconstruction diagnostic has been developed for the ISTTOK tokamak, based on three linear pinhole cameras each with ten lines of sight. The plasma emissivity in a poloidal cross section is computed locally on a submillisecond time scale, using a Fourier-Bessel algorithm, allowing the use of the output signals for active plasma position control. The data acquisition and reconstruction (DAR) system is based on ATCA technology and consists of one acquisition board with integrated field programmable gate array (FPGA) capabilities and a dual-core Pentium module running real-time application interface (RTAI) Linux. In this paper, the DAR real-time firmware/software implementation is presented, based on (i) front-end digital processing in the FPGA; (ii) a device driver specially developed for the board which enables streaming data acquisition to the host GPU; and (iii) a fast reconstruction algorithm running in Linux RTAI. This system behaves as a module of the central ISTTOK control and data acquisition system (FIRESIGNAL). Preliminary results of the above experimental setup are presented and a performance benchmarking against the magnetic coil diagnostic is shown.

Improved image quality with simultaneously reduced radiation exposure: Knowledge-based iterative model reconstruction algorithms for coronary CT angiography in a clinical setting.

PubMed

André, Florian; Fortner, Philipp; Vembar, Mani; Mueller, Dirk; Stiller, Wolfram; Buss, Sebastian J; Kauczor, Hans-Ulrich; Katus, Hugo A; Korosoglou, Grigorios

The aim of this study was to assess the potential for radiation dose reduction using knowledge-based iterative model reconstruction (K-IMR) algorithms in combination with ultra-low dose body mass index (BMI)-adapted protocols in coronary CT angiography (coronary CTA). Forty patients undergoing clinically indicated coronary CTA were randomly assigned to two groups with BMI-adapted (I: <25.0 kg/m 2 , II: <28.0 kg/m 2 , III: <30.0 kg/m 2 , IV: ≥30.0 kg/m 2 ) low dose (LD, I: 100kV p /75 mAs, II: 100kV p /100 mAs, III: 100kV p /150 mAs, IV: 120kV p /150 mAs, n = 20) or ultra-low dose (ULD, I: 100kV p /50 mAs, II: 100kV p /75 mAs, III: 100kV p /100 mAs, IV: 120kV p /100 mAs, n = 20) protocols. Prospectively-triggered coronary CTA was performed using a 256-MDCT with the lowest reasonable scan length. Images were generated with filtered back projection (FBP), a noise-reducing hybrid iterative algorithm (iD, levels 2/5) and K-IMR using cardiac routine (CR) and cardiac sharp settings, levels 1-3. Groups were comparable regarding anthropometric parameters, heart rate, and scan length. The use of ULD protocols resulted in a significant reduction of radiation exposure (0.7 (0.6-0.9) mSv vs. 1.1 (0.9-1.7) mSv; p < 0.02). Image quality was significantly better in the ULD group using K-IMR CR 1 compared to FBP, iD 2 and iD 5 in the LD group, resulting in fewer non-diagnostic coronary segments (2.4% vs. 11.6%, 9.2% and 6.1%; p < 0.05). The combination of K-IMR with BMI-adapted ULD protocols results in significant radiation dose savings while simultaneously improving image quality compared to LD protocols with FBP or hybrid iterative algorithms. Therefore, K-IMR allows for coronary CTA examinations with high diagnostic value and very low radiation exposure in clinical routine. Copyright © 2017 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Autoimmune diagnostics: the technology, the strategy and the clinical governance.

PubMed

Bizzaro, Nicola; Tozzoli, Renato; Villalta, Danilo

2015-02-01

In recent years, there has been a profound change in autoimmune diagnostics. From long, tiring and inaccurate manual methods, the art of diagnostics has turned to modern, rapid and automated technology. New antibody tests have been developed, and almost all autoimmune diseases now have some specific diagnostic markers. The current need to make the most of available economic and human resources has led to the production of diagnostic algorithms and guidelines designated for optimal strategic use of the tests and to increase the diagnostic appropriateness. An important role in this scenario was assumed by the laboratory autoimmunologist, whose task is not only to govern the analytical phase, but also to help clinicians in correctly choosing the most suitable test for each clinical situation and provide consultancy support. In this review, we summarize recent advances in technology, describe the diagnostic strategies and highlight the current role of the laboratory autoimmunologist in the clinical governance of autoimmune diagnostics.

Simple Estimation of Incident HIV Infection Rates in Notification Cohorts Based on Window Periods of Algorithms for Evaluation of Line-Immunoassay Result Patterns

PubMed Central

Schüpbach, Jörg; Gebhardt, Martin D.; Scherrer, Alexandra U.; Bisset, Leslie R.; Niederhauser, Christoph; Regenass, Stephan; Yerly, Sabine; Aubert, Vincent; Suter, Franziska; Pfister, Stefan; Martinetti, Gladys; Andreutti, Corinne; Klimkait, Thomas; Brandenberger, Marcel; Günthard, Huldrych F.

2013-01-01

Background Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods. Methods We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship ‘Prevalence  =  Incidence x Duration’ in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship ‘incident  =  true incident + false incident’ and also to the IIR derived from the BED incidence assay. Results Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R2 = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods. Conclusions IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts. PMID:23990968

The value of cyclooxygenase-2 expression in differentiating between early melanomas and histopathologically difficult types of benign human skin lesions.

PubMed

Kuźbicki, Łukasz; Lange, Dariusz; Strączyńska-Niemiec, Anita; Chwirot, Barbara W

2012-02-01

Early cutaneous melanomas may present a substantial diagnostic challenge. We have already reported that expression of cyclooxygenase-2 (COX-2) may be useful for differentiating between cutaneous melanomas and naevi. The purpose of this study was to examine the value of COX-2 in a challenging task of differential diagnosis of early melanomas and melanocytic naevi considered by histopathologists as morphologically difficult to unequivocally diagnose as benign lesions. The material for the study comprised formalin-fixed paraffin-embedded samples of 46 naevi (including 27 cases of dysplastic, Spitz and Reed naevi) and 30 early human cutaneous melanomas. The expression of COX-2 was detected immunohistochemically. Melanomas expressed COX-2 significantly more strongly compared with naevi. The test, on the basis of determination of the percentage fractions of COX-2-positive cells, allows for differentiation of early skin melanomas and naevi with high sensitivity and specificity. Receiver operating characteristic analysis of the test results yielded areas under receiver operating characteristics curves (AUC)=0.946±0.030 for central regions and AUC=0.941±0.031 for the peripheries of the lesions. The performance of the test in differentiating between melanomas and the naevi group comprising dysplastic, Spitz and Reed naevi was also good, with AUC=0.933±0.034 and 0.923±0.037 for the central and the border regions of the lesions, respectively. Using a more complex diagnostic algorithm also accounting for the staining intensity did not result in an improvement in the resolving power of the assay. A diagnostic algorithm using differences in the percentage fractions of cells expressing COX-2 may serve as a useful tool in aiding the differential diagnosis of 'histopathologically difficult' benign melanocytic skin lesions and early melanomas.

On the performance of SART and ART algorithms for microwave imaging

NASA Astrophysics Data System (ADS)

Aprilliyani, Ria; Prabowo, Rian Gilang; Basari

2018-02-01

The development of advanced technology leads to the change of human lifestyle in current society. One of the disadvantage impact is arising the degenerative diseases such as cancers and tumors, not just common infectious diseases. Every year, victims of cancers and tumors grow significantly leading to one of the death causes in the world. In early stage, cancer/tumor does not have definite symptoms, but it will grow abnormally as tissue cells and damage normal tissue. Hence, early cancer detection is required. Some common diagnostics modalities such as MRI, CT and PET are quite difficult to be operated in home or mobile environment such as ambulance. Those modalities are also high cost, unpleasant, complex, less safety and harder to move. Hence, this paper proposes a microwave imaging system due to its portability and low cost. In current study, we address on the performance of simultaneous algebraic reconstruction technique (SART) algorithm that was applied in microwave imaging. In addition, SART algorithm performance compared with our previous work on algebraic reconstruction technique (ART), in order to have performance comparison, especially in the case of reconstructed image quality. The result showed that by applying SART algorithm on microwave imaging, suspicious cancer/tumor can be detected with better image quality.

Magnetic resonance imaging diffusion tensor tractography: evaluation of anatomic accuracy of different fiber tracking software packages.

PubMed

Feigl, Guenther C; Hiergeist, Wolfgang; Fellner, Claudia; Schebesch, Karl-Michael M; Doenitz, Christian; Finkenzeller, Thomas; Brawanski, Alexander; Schlaier, Juergen

2014-01-01

Diffusion tensor imaging (DTI)-based tractography has become an integral part of preoperative diagnostic imaging in many neurosurgical centers, and other nonsurgical specialties depend increasingly on DTI tractography as a diagnostic tool. The aim of this study was to analyze the anatomic accuracy of visualized white matter fiber pathways using different, readily available DTI tractography software programs. Magnetic resonance imaging scans of the head of 20 healthy volunteers were acquired using a Siemens Symphony TIM 1.5T scanner and a 12-channel head array coil. The standard settings of the scans in this study were 12 diffusion directions and 5-mm slices. The fornices were chosen as an anatomic structure for the comparative fiber tracking. Identical data sets were loaded into nine different fiber tracking packages that used different algorithms. The nine software packages and algorithms used were NeuroQLab (modified tensor deflection [TEND] algorithm), Sörensen DTI task card (modified streamline tracking technique algorithm), Siemens DTI module (modified fourth-order Runge-Kutta algorithm), six different software packages from Trackvis (interpolated streamline algorithm, modified FACT algorithm, second-order Runge-Kutta algorithm, Q-ball [FACT algorithm], tensorline algorithm, Q-ball [second-order Runge-Kutta algorithm]), DTI Query (modified streamline tracking technique algorithm), Medinria (modified TEND algorithm), Brainvoyager (modified TEND algorithm), DTI Studio modified FACT algorithm, and the BrainLab DTI module based on the modified Runge-Kutta algorithm. Three examiners (a neuroradiologist, a magnetic resonance imaging physicist, and a neurosurgeon) served as examiners. They were double-blinded with respect to the test subject and the fiber tracking software used in the presented images. Each examiner evaluated 301 images. The examiners were instructed to evaluate screenshots from the different programs based on two main criteria: (i) anatomic accuracy of the course of the displayed fibers and (ii) number of fibers displayed outside the anatomic boundaries. The mean overall grade for anatomic accuracy was 2.2 (range, 1.1-3.6) with a standard deviation (SD) of 0.9. The mean overall grade for incorrectly displayed fibers was 2.5 (range, 1.6-3.5) with a SD of 0.6. The mean grade of the overall program ranking was 2.3 with a SD of 0.6. The overall mean grade of the program ranked number one (NeuroQLab) was 1.7 (range, 1.5-2.8). The mean overall grade of the program ranked last (BrainLab iPlan Cranial 2.6 DTI Module) was 3.3 (range, 1.7-4). The difference between the mean grades of these two programs was statistically highly significant (P < 0.0001). There was no statistically significant difference between the programs ranked 1-3: NeuroQLab, Sörensen DTI Task Card, and Siemens DTI module. The results of this study show that there is a statistically significant difference in the anatomic accuracy of the tested DTI fiber tracking programs. Although incorrectly displayed fibers could lead to wrong conclusions in the neurosciences field, which relies heavily on this noninvasive imaging technique, incorrectly displayed fibers in neurosurgery could lead to surgical decisions potentially harmful for the patient if used without intraoperative cortical stimulation. DTI fiber tracking presents a valuable noninvasive preoperative imaging tool, which requires further validation after important standardization of the acquisition and processing techniques currently available. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparison of the Vidas C. difficile GDH Automated Enzyme-Linked Fluorescence Immunoassay (ELFA) with Another Commercial Enzyme Immunoassay (EIA) (Quik Chek-60), Two Selective Media, and a PCR Assay for gluD for Detection of Clostridium difficile in Fecal Samples.

PubMed

Davies, K A; Berry, C E; Morris, K A; Smith, R; Young, S; Davis, T E; Fuller, D D; Buckner, R J; Wilcox, M H

2015-06-01

Prevention and management of Clostridium difficile infection (CDI) can be improved by rapid and reliable diagnostics. The Vidas C. difficile glutamate dehydrogenase assay had performance comparable to that of the Quik Chek-60 assay (overall agreement, 95%) and a sensitivity of >93%; thus, it is suitable as the first test in two-stage algorithms for a CDI diagnosis. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

A Markov Chain-based quantitative study of angular distribution of photons through turbid slabs via isotropic light scattering

NASA Astrophysics Data System (ADS)

Li, Xuesong; Northrop, William F.

2016-04-01

This paper describes a quantitative approach to approximate multiple scattering through an isotropic turbid slab based on Markov Chain theorem. There is an increasing need to utilize multiple scattering for optical diagnostic purposes; however, existing methods are either inaccurate or computationally expensive. Here, we develop a novel Markov Chain approximation approach to solve multiple scattering angular distribution (AD) that can accurately calculate AD while significantly reducing computational cost compared to Monte Carlo simulation. We expect this work to stimulate ongoing multiple scattering research and deterministic reconstruction algorithm development with AD measurements.

Tuberculosis Case Finding With Combined Rapid Point-of-Care Assays (Xpert MTB/RIF and Determine TB LAM) in HIV-Positive Individuals Starting Antiretroviral Therapy in Mozambique.

PubMed

Floridia, Marco; Ciccacci, Fausto; Andreotti, Mauro; Hassane, Archa; Sidumo, Zita; Magid, Nurja A; Sotomane, Horacio; David, Muhlavasse; Mutemba, Elsa; Cebola, Junia; Mugunhe, Remigio Josè; Riccardi, Fabio; Marazzi, Maria Cristina; Giuliano, Marina; Palombi, Leonardo; Mancinelli, Sandro

2017-11-13

Tuberculosis is a major health concern in several countries, and effective diagnostic algorithms for use in human immunodeficiency virus (HIV)-positive patients are urgently needed. At prescription of antiretroviral therapy, all patients in 3 Mozambican health centers were screened for tuberculosis, with a combined approach: World Health Organization (WHO) 4-symptom screening (fever, cough, night sweats, and weight loss), a rapid test detecting mycobacterial lipoarabinomannan in urine (Determine TB LAM), and a molecular assay performed on a sputum sample (Xpert MTB/RIF; repeated if first result was negative). Patients with positive LAM or Xpert MTB/RIF results were referred for tuberculosis treatment. Among 972 patients with a complete diagnostic algorithm (58.5% female; median CD4 cell count, 278/μL; WHO HIV stage I, 66.8%), 98 (10.1%) tested positive with Xpert (90, 9.3%) or LAM (34, 3.5%) assays. Compared with a single-test Xpert strategy, dual Xpert tests improved case finding by 21.6%, LAM testing alone improved it by 13.5%, and dual Xpert tests plus LAM testing improved it by 32.4%. Rifampicin resistance in Xpert-positive patients was infrequent (2.5%). Among patients with positive results, 22 of 98 (22.4%) had no symptoms at WHO 4-symptom screening. Patients with tuberculosis diagnosed had significantly lower CD4 cell counts and hemoglobin levels, more advanced WHO stage, and higher HIV RNA levels. Fifteen (15.3%) did not start tuberculosis treatment, mostly owing to rapidly deteriorating clinical conditions or logistical constraints. The median interval between start of the diagnostic algorithm and start of tuberculosis treatment was 7 days. The prevalence of tuberculosis among Mozambican HIV-positive patients starting antiretroviral therapy was 10%, with limited rifampicin resistance. Use of combined point-of-care tests increased case finding, with a short time to treatment. Interventions are needed to remove logistical barriers and prevent presentation in very advanced HIV/tuberculosis disease. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Toward DSM-V: an item response theory analysis of the diagnostic process for DSM-IV alcohol abuse and dependence in adolescents.

PubMed

Gelhorn, Heather; Hartman, Christie; Sakai, Joseph; Stallings, Michael; Young, Susan; Rhee, Soo Hyun; Corley, Robin; Hewitt, John; Hopfer, Christian; Crowley, Thomas

2008-11-01

Item response theory analyses were used to examine alcohol abuse and dependence symptoms and diagnoses in adolescents. Previous research suggests that the DSM-IV alcohol use disorder (AUD) symptoms in adolescents may be characterized by a single dimension. The present study extends prior research with a larger and more comprehensive sample and an examination of an alternative diagnostic algorithm for AUDs. Approximately 5,587 adolescents between the ages of 12 and 18 years from adjudicated, clinical, and community samples were administered structured clinical interviews. Analyses were conducted to examine the severity of alcohol abuse and dependence symptoms and the severity of alcohol use problems (AUDs) within the diagnostic categories created by the DSM-IV. Although the DSM-IV diagnostic categories differ in severity of AUDs, there is substantial overlap and inconsistency in AUD severity of persons across these categories. Item Response Theory-based AUD severity estimates suggest that many persons diagnosed with abuse have AUD severity greater than persons with dependence. Similarly, many persons who endorse some symptoms but do not qualify for a diagnosis (i.e., diagnostic orphans) have more severe AUDs than persons with an abuse diagnosis. Additionally, two dependence items, "tolerance" and "larger/longer," show differences in severity between samples. The distinction between DSM-IV abuse and dependence based on severity can be improved using an alternative diagnostic algorithm that considers all of the alcohol abuse and dependence symptoms conjointly.

UWGSP6: a diagnostic radiology workstation of the future

NASA Astrophysics Data System (ADS)

Milton, Stuart W.; Han, Sang; Choi, Hyung-Sik; Kim, Yongmin

1993-06-01

The Univ. of Washington's Image Computing Systems Lab. (ICSL) has been involved in research into the development of a series of PACS workstations since the middle 1980's. The most recent research, a joint UW-IBM project, attempted to create a diagnostic radiology workstation using an IBM RISC System 6000 (RS6000) computer workstation and the X-Window system. While the results are encouraging, there are inherent limitations in the workstation hardware which prevent it from providing an acceptable level of functionality for diagnostic radiology. Realizing the RS6000 workstation's limitations, a parallel effort was initiated to design a workstation, UWGSP6 (Univ. of Washington Graphics System Processor #6), that provides the required functionality. This paper documents the design of UWGSP6, which not only addresses the requirements for a diagnostic radiology workstation in terms of display resolution, response time, etc., but also includes the processing performance necessary to support key functions needed in the implementation of algorithms for computer-aided diagnosis. The paper includes a description of the workstation architecture, and specifically its image processing subsystem. Verification of the design through hardware simulation is then discussed, and finally, performance of selected algorithms based on detailed simulation is provided.

Plantar fasciitis in athletes: diagnostic and treatment strategies. A systematic review

PubMed Central

Petraglia, Federica; Ramazzina, Ileana; Costantino, Cosimo

2017-01-01

Summary Background: Plantar fasciitis (PF) is reported in different sports mainly in running and soccer athletes. Purpose of this study is to conduct a systematic review of published literature concerning the diagnosis and treatment of PF in both recreational and élite athletes. The review was conducted and reported in accordance with the PRISMA statement. Methods: The following electronic databases were searched: PubMed, Cochrane Library and Scopus. As far as PF diagnosis, we investigated the electronic databases from January 2006 to June 2016, whereas in considering treatments all data in literature were investigated. Results: For both diagnosis and treatment, 17 studies matched inclusion criteria. The results have highlighted that the most frequently used diagnostic techniques were Ultrasonography and Magnetic Resonance Imaging. Conventional, complementary, and alternative treatment approaches were assessed. Conclusions: In reviewing literature, we were unable to find any specific diagnostic algorithm for PF in athletes, due to the fact that no different diagnostic strategies were used for athletes and non-athletes. As for treatment, a few literature data are available and it makes difficult to suggest practice guidelines. Specific studies are necessary to define the best treatment algorithm for both recreational and élite athletes. Level of evidence: Ib. PMID:28717618

Multiplex PCR Tests for Detection of Pathogens Associated with Gastroenteritis

PubMed Central

Zhang, Hongwei; Morrison, Scott; Tang, Yi-Wei

2016-01-01

Synopsis A wide range of enteric pathogens can cause infectious gastroenteritis. Conventional diagnostic algorithms including culture, biochemical identification, immunoassay and microscopic examination are time consuming and often lack sensitivity and specificity. Advances in molecular technology have as allowed its use as clinical diagnostic tools. Multiplex PCR based testing has made its way to gastroenterology diagnostic arena in recent years. In this article we present a review of recent laboratory developed multiplex PCR tests and current commercial multiplex gastrointestinal pathogen tests. We will focus on two FDA cleared commercial syndromic multiplex tests: Luminex xTAG GPP and Biofire FimArray GI test. These multiplex tests can detect and identify multiple enteric pathogens in one test and provide results within hours. Multiplex PCR tests have shown superior sensitivity to conventional methods for detection of most pathogens. The high negative predictive value of these multiplex tests has led to the suggestion that they be used as screening tools especially in outbreaks. Although the clinical utility and benefit of multiplex PCR test are to be further investigated, implementing these multiplex PCR tests in gastroenterology diagnostic algorithm has the potential to improve diagnosis of infectious gastroenteritis. PMID:26004652

Addressing the Real-World Challenges in the Development of Propulsion IVHM Technology Experiment (PITEX)

NASA Technical Reports Server (NTRS)

Maul, William A.; Chicatelli, Amy; Fulton, Christopher E.; Balaban, Edward; Sweet, Adam; Hayden, Sandra Claire; Bajwa, Anupa

2005-01-01

The Propulsion IVHM Technology Experiment (PITEX) has been an on-going research effort conducted over several years. PITEX has developed and applied a model-based diagnostic system for the main propulsion system of the X-34 reusable launch vehicle, a space-launch technology demonstrator. The application was simulation-based using detailed models of the propulsion subsystem to generate nominal and failure scenarios during captive carry, which is the most safety-critical portion of the X-34 flight. Since no system-level testing of the X-34 Main Propulsion System (MPS) was performed, these simulated data were used to verify and validate the software system. Advanced diagnostic and signal processing algorithms were developed and tested in real-time on flight-like hardware. In an attempt to expose potential performance problems, these PITEX algorithms were subject to numerous real-world effects in the simulated data including noise, sensor resolution, command/valve talkback information, and nominal build variations. The current research has demonstrated the potential benefits of model-based diagnostics, defined the performance metrics required to evaluate the diagnostic system, and studied the impact of real-world challenges encountered when monitoring propulsion subsystems.

Syndrome Diagnosis: Human Intuition or Machine Intelligence?

PubMed Central

Braaten, Øivind; Friestad, Johannes

2008-01-01

The aim of this study was to investigate whether artificial intelligence methods can represent objective methods that are essential in syndrome diagnosis. Most syndromes have no external criterion standard of diagnosis. The predictive value of a clinical sign used in diagnosis is dependent on the prior probability of the syndrome diagnosis. Clinicians often misjudge the probabilities involved. Syndromology needs objective methods to ensure diagnostic consistency, and take prior probabilities into account. We applied two basic artificial intelligence methods to a database of machine-generated patients - a ‘vector method’ and a set method. As reference methods we ran an ID3 algorithm, a cluster analysis and a naive Bayes’ calculation on the same patient series. The overall diagnostic error rate for the the vector algorithm was 0.93%, and for the ID3 0.97%. For the clinical signs found by the set method, the predictive values varied between 0.71 and 1.0. The artificial intelligence methods that we used, proved simple, robust and powerful, and represent objective diagnostic methods. PMID:19415142

Syndrome diagnosis: human intuition or machine intelligence?

PubMed

Braaten, Oivind; Friestad, Johannes

2008-01-01

The aim of this study was to investigate whether artificial intelligence methods can represent objective methods that are essential in syndrome diagnosis. Most syndromes have no external criterion standard of diagnosis. The predictive value of a clinical sign used in diagnosis is dependent on the prior probability of the syndrome diagnosis. Clinicians often misjudge the probabilities involved. Syndromology needs objective methods to ensure diagnostic consistency, and take prior probabilities into account. We applied two basic artificial intelligence methods to a database of machine-generated patients - a 'vector method' and a set method. As reference methods we ran an ID3 algorithm, a cluster analysis and a naive Bayes' calculation on the same patient series. The overall diagnostic error rate for the the vector algorithm was 0.93%, and for the ID3 0.97%. For the clinical signs found by the set method, the predictive values varied between 0.71 and 1.0. The artificial intelligence methods that we used, proved simple, robust and powerful, and represent objective diagnostic methods.

[Iron Deficiency in Chronic Heart Failure: Diagnostic Algorithm and Present-Day Therapeutic Options].

PubMed

Doehner, Wolfram; Blankenberg, Stefan; Erdmann, Erland; Ertl, Georg; Hasenfuß, Gerd; Landmesser, Ulf; Pieske, Burkert; Schieffer, Bernhard; Schunkert, Heribert; von Haehling, Stephan; Zeiher, Andreas; Anker, Stefan D

2017-05-01

Iron deficiency (ID) occurs in up to 50% of patients with heart failure (HF). Even without presence of anaemia ID contributes to more severe symptoms, increased hospitalization and mortality. A number of randomized controlled trials demonstrated the clinical benefit of replenishment of iron stores with improvement of symptoms and fewer hospitalizations. Assessment of iron status should therefore become routine assessment in newly diagnosed and in symptomatic patients with HF. ID can be identified with simple and straightforward diagnostic steps. Assessment of Ferritin (indicating iron stores) and transferrin saturation (TSAT, indication capability to mobilise internal iron stores) are sufficient to detect ID. In this review a plain diagnostic algorithm for ID is suggested. Confounding factors for diagnosis and adequate treatment of ID in HF are discussed. A regular workup for iron deficiency parameters may benefit patients with heart failure by providing symptomatic improvements and fewer hospitalizations. © Georg Thieme Verlag KG Stuttgart · New York.

Recent Trends in the Serologic Diagnosis of Syphilis

PubMed Central

Singh, Ameeta E.

2014-01-01

Complexities in the diagnosis of syphilis continue to challenge clinicians. While direct tests (e.g., microscopy or PCR) are helpful in early syphilis, the mainstay of diagnosis remains serologic tests. The traditional algorithm using a nontreponemal test (NTT) followed by a treponemal test (TT) remains the standard in many parts of the world. More recently, the ability to automate the TT has led to the increasingly widespread use of reverse algorithms using treponemal enzyme immunoassays (EIAs). Rapid, point-of-care TTs are in widespread use in developing countries because of low cost, ease of use, and reasonable performance. However, none of the current diagnostic algorithms are able to distinguish current from previously treated infections. In addition, the reversal of traditional syphilis algorithms has led to uncertainty in the clinical management of patients. The interpretation of syphilis tests is further complicated by the lack of a reliable gold standard for syphilis diagnostics, and the newer tests can result in false-positive reactions similar to those seen with older tests. Little progress has been made in the area of serologic diagnostics for congenital syphilis, which requires assessment of maternal treatment and serologic response as well as clinical and laboratory investigation of the neonate for appropriate management. The diagnosis of neurosyphilis continues to require the collection of cerebrospinal fluid for a combination of NTT and TT, and, while newer treponemal EIAs look promising, more studies are needed to confirm their utility. This article reviews current tests and discusses current controversies in syphilis diagnosis, with a focus on serologic tests. PMID:25428245

Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

PubMed

Awaysheh, Abdullah; Wilcke, Jeffrey; Elvinger, François; Rees, Loren; Fan, Weiguo; Zimmerman, Kurt L

2016-11-01

Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p < 0.0001). The areas under the receiver-operating characteristic curve for classifying cases into the 3 categories was 83% by naive Bayes, 79% by decision tree, and 82% by artificial neural networks. Prediction models using machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. © 2016 The Author(s).

Advanced Imaging Adds Little Value in the Diagnosis of Femoroacetabular Impingement Syndrome.

PubMed

Cunningham, Daniel J; Paranjape, Chinmay S; Harris, Joshua D; Nho, Shane J; Olson, Steven A; Mather, Richard C

2017-12-20

Femoroacetabular impingement (FAI) syndrome is an increasingly recognized source of hip pain and disability in young active adults. In order to confirm the diagnosis, providers often supplement physical examination maneuvers and radiographs with intra-articular hip injection, magnetic resonance imaging (MRI), or magnetic resonance arthrography (MRA). Since diagnostic imaging represents the fastest rising cost segment in U.S. health care, there is a need for value-driven diagnostic algorithms. The purpose of this study was to identify cost-effective diagnostic strategies for symptomatic FAI, comparing history and physical examination (H&P) alone (utilizing only radiographic imaging) with supplementation with injection, MRI, or MRA. A simple-chain decision model run as a cost-utility analysis was constructed to assess the diagnostic value of the MRI, MRA, and injection that are added to the H&P and radiographs in diagnosing symptomatic FAI. Strategies were compared using the incremental cost-utility ratio (ICUR) with a willingness to pay (WTP) of $100,000/QALY (quality-adjusted life year). Direct costs were measured using the Humana database (PearlDiver). Diagnostic test accuracy, treatment outcome probabilities, and utilities were extracted from the literature. H&P with and without supplemental diagnostic injection was the most cost-effective. Adjunct injection was preferred in situations with a WTP of >$60,000/QALY, low examination sensitivity, and high FAI prevalence. With low disease prevalence and low examination sensitivity, as may occur in a general practitioner's office, H&P with injection was the most cost-effective strategy, whereas in the reciprocal scenario, H&P with injection was only favored at exceptionally high WTP (∼$990,000). H&P and radiographs with supplemental diagnostic injection are preferred over advanced imaging, even with reasonable deviations from published values of disease prevalence, test sensitivity, and test specificity. Providers with low examination sensitivity in situations with low disease prevalence may benefit most from including injection in their diagnostic strategy. Providers with high examination sensitivity in situations with high disease prevalence may not benefit from including injection in their diagnostic strategy. Providers should not routinely rely on advanced imaging to diagnose FAI syndrome, although advanced imaging may have a role in challenging clinical scenarios. Economic and Decision Analysis Level IV. See Instructions for Authors for a complete description of levels of evidence.

Proposal for a standardised identification of the mono-exponential terminal phase for orally administered drugs.

PubMed

Scheerans, Christian; Derendorf, Hartmut; Kloft, Charlotte

2008-04-01

The area under the plasma concentration-time curve from time zero to infinity (AUC(0-inf)) is generally considered to be the most appropriate measure of total drug exposure for bioavailability/bioequivalence studies of orally administered drugs. However, the lack of a standardised method for identifying the mono-exponential terminal phase of the concentration-time curve causes variability for the estimated AUC(0-inf). The present investigation introduces a simple method, called the two times t(max) method (TTT method) to reliably identify the mono-exponential terminal phase in the case of oral administration. The new method was tested by Monte Carlo simulation in Excel and compared with the adjusted r squared algorithm (ARS algorithm) frequently used in pharmacokinetic software programs. Statistical diagnostics of three different scenarios, each with 10,000 hypothetical patients showed that the new method provided unbiased average AUC(0-inf) estimates for orally administered drugs with a monophasic concentration-time curve post maximum concentration. In addition, the TTT method generally provided more precise estimates for AUC(0-inf) compared with the ARS algorithm. It was concluded that the TTT method is a most reasonable tool to be used as a standardised method in pharmacokinetic analysis especially bioequivalence studies to reliably identify the mono-exponential terminal phase for orally administered drugs showing a monophasic concentration-time profile.

Practical application of contrast-enhanced magnetic resonance mammography [CE-MRM] by an algorithm combining morphological and enhancement patterns.

PubMed

Potente, Giuseppe; Messineo, Daniela; Maggi, Claudia; Savelli, Sara

2009-03-01

The purpose of this article is to report our practical utilization of dynamic contrast-enhanced magnetic resonance mammography [DCE-MRM] in the diagnosis of breast lesions. In many European centers, was preferred a high-temporal acquisition of both breasts simultaneously in a large FOV. We preferred to scan single breasts, with the aim to combine the analysis of the contrast intake and washout with the morphological evaluation of breast lesions. We followed an interpretation model, based upon a diagnostic algorithm, which combined contrast enhancement with morphological evaluation, in order to increase our confidence in diagnosis. DCE-MRM with our diagnostic algorithm has identified 179 malignant and 41 benign lesions; final outcome has identified 178 malignant and 42 benign lesions, 3 false positives and 2 false negatives. Sensitivity of CE-MRM was 98.3%; specificity, 95.1%; positive predictive value, 98.9%; negative predictive value, 92.8% and accuracy, 97.7%.

Automated frame selection process for high-resolution microendoscopy

NASA Astrophysics Data System (ADS)

Ishijima, Ayumu; Schwarz, Richard A.; Shin, Dongsuk; Mondrik, Sharon; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-04-01

We developed an automated frame selection algorithm for high-resolution microendoscopy video sequences. The algorithm rapidly selects a representative frame with minimal motion artifact from a short video sequence, enabling fully automated image analysis at the point-of-care. The algorithm was evaluated by quantitative comparison of diagnostically relevant image features and diagnostic classification results obtained using automated frame selection versus manual frame selection. A data set consisting of video sequences collected in vivo from 100 oral sites and 167 esophageal sites was used in the analysis. The area under the receiver operating characteristic curve was 0.78 (automated selection) versus 0.82 (manual selection) for oral sites, and 0.93 (automated selection) versus 0.92 (manual selection) for esophageal sites. The implementation of fully automated high-resolution microendoscopy at the point-of-care has the potential to reduce the number of biopsies needed for accurate diagnosis of precancer and cancer in low-resource settings where there may be limited infrastructure and personnel for standard histologic analysis.

Overcoming limitations of model-based diagnostic reasoning systems

NASA Technical Reports Server (NTRS)

Holtzblatt, Lester J.; Marcotte, Richard A.; Piazza, Richard L.

1989-01-01

The development of a model-based diagnostic system to overcome the limitations of model-based reasoning systems is discussed. It is noted that model-based reasoning techniques can be used to analyze the failure behavior and diagnosability of system and circuit designs as part of the system process itself. One goal of current research is the development of a diagnostic algorithm which can reason efficiently about large numbers of diagnostic suspects and can handle both combinational and sequential circuits. A second goal is to address the model-creation problem by developing an approach for using design models to construct the GMODS model in an automated fashion.

A diagnostic approach to hemochromatosis

PubMed Central

Tavill, Anthony S; Adams, Paul C

2006-01-01

In the present clinical review, a diagnostic approach to hemochromatosis is discussed from the perspective of two clinicians with extensive experience in this area. The introduction of genetic testing and large-scale population screening studies have broadened our understanding of the clinical expression of disease and the utility of biochemical iron tests for the detection of disease and for the assessment of disease severity. Liver biopsy has become more of a prognostic test than a diagnostic test. The authors offer a stepwise, diagnostic algorithm based on current evidence-based data, that they regard as most cost-effective. An early diagnosis can lead to phlebotomy therapy to prevent the development of cirrhosis. PMID:16955151

Cutaneous T cell lymphoma: Current practices in blood assessment and the utility of T-cell receptor Vβ chain restriction

PubMed Central

Gibson, Juliet F; Huang, Jing; Liu, Kristina J; Carlson, Kacie R; Foss, Francine; Choi, Jaehyuk; Edelson, Richard; Hussong, Jerry W.; Mohl, Ramsey; Hill, Sally; Girardi, Sally

2016-01-01

Background Accurate quantification of malignant cells in the peripheral blood of patients with cutaneous T cell lymphoma (CTCL) is important for early detection, prognosis, and monitoring disease burden. Objective Determine the spectrum of current clinical practices; critically evaluate elements of current ISCL B1 and B2 staging criteria; and assess the potential role of TCR-Vβ analysis by flow cytometry. Methods We assessed current clinical practices by survey, and performed a retrospective analysis of 161 patients evaluated at Yale (2011-2014) to compare the sensitivity, specificity, PPV, and NPV of parameters for ISCL B2 staging. Results There was heterogeneity in clinical practices among institutions. ISCL B1 criteria did not capture five Yale cohort patients with immunophenotypic abnormalities who later progressed. TCR-Vβ testing was more specific than PCR and aided diagnosis in detecting clonality, but was of limited benefit in quantification of tumor burden. Limitations Because of limited follow-up involving a single center, further investigation will be necessary to conclude whether our proposed diagnostic algorithm is of general clinical benefit. Conclusion We propose further study of “modified B1 criteria”: CD4/CD8 ratio ≥5, %CD4+/CD26- ≥ 20%, %CD4+/CD7- ≥ 20%, with evidence of clonality. TCR-Vβ testing should be considered in future diagnostic and staging algorithms. PMID:26874819

[Imputation methods for missing data in educational diagnostic evaluation].

PubMed

Fernández-Alonso, Rubén; Suárez-Álvarez, Javier; Muñiz, José

2012-02-01

In the diagnostic evaluation of educational systems, self-reports are commonly used to collect data, both cognitive and orectic. For various reasons, in these self-reports, some of the students' data are frequently missing. The main goal of this research is to compare the performance of different imputation methods for missing data in the context of the evaluation of educational systems. On an empirical database of 5,000 subjects, 72 conditions were simulated: three levels of missing data, three types of loss mechanisms, and eight methods of imputation. The levels of missing data were 5%, 10%, and 20%. The loss mechanisms were set at: Missing completely at random, moderately conditioned, and strongly conditioned. The eight imputation methods used were: listwise deletion, replacement by the mean of the scale, by the item mean, the subject mean, the corrected subject mean, multiple regression, and Expectation-Maximization (EM) algorithm, with and without auxiliary variables. The results indicate that the recovery of the data is more accurate when using an appropriate combination of different methods of recovering lost data. When a case is incomplete, the mean of the subject works very well, whereas for completely lost data, multiple imputation with the EM algorithm is recommended. The use of this combination is especially recommended when data loss is greater and its loss mechanism is more conditioned. Lastly, the results are discussed, and some future lines of research are analyzed.

Molecular Diagnosis and Biomarker Identification on SELDI proteomics data by ADTBoost method.

PubMed

Wang, Lu-Yong; Chakraborty, Amit; Comaniciu, Dorin

2005-01-01

Clinical proteomics is an emerging field that will have great impact on molecular diagnosis, identification of disease biomarkers, drug discovery and clinical trials in the post-genomic era. Protein profiling in tissues and fluids in disease and pathological control and other proteomics techniques will play an important role in molecular diagnosis with therapeutics and personalized healthcare. We introduced a new robust diagnostic method based on ADTboost algorithm, a novel algorithm in proteomics data analysis to improve classification accuracy. It generates classification rules, which are often smaller and easier to interpret. This method often gives most discriminative features, which can be utilized as biomarkers for diagnostic purpose. Also, it has a nice feature of providing a measure of prediction confidence. We carried out this method in amyotrophic lateral sclerosis (ALS) disease data acquired by surface enhanced laser-desorption/ionization-time-of-flight mass spectrometry (SELDI-TOF MS) experiments. Our method is shown to have outstanding prediction capacity through the cross-validation, ROC analysis results and comparative study. Our molecular diagnosis method provides an efficient way to distinguish ALS disease from neurological controls. The results are expressed in a simple and straightforward alternating decision tree format or conditional format. We identified most discriminative peaks in proteomic data, which can be utilized as biomarkers for diagnosis. It will have broad application in molecular diagnosis through proteomics data analysis and personalized medicine in this post-genomic era.

Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection

ERIC Educational Resources Information Center

Zijlstra, Wobbe P.; van der Ark, L. Andries; Sijtsma, Klaas

2011-01-01

Exploratory Mokken scale analysis (MSA) is a popular method for identifying scales from larger sets of items. As with any statistical method, in MSA the presence of outliers in the data may result in biased results and wrong conclusions. The forward search algorithm is a robust diagnostic method for outlier detection, which we adapt here to…

Benefits and challenges of molecular diagnostics for childhood tuberculosis.

PubMed

Gutierrez, Cristina

2016-12-01

Expanding tuberculosis (TB)-diagnostic services, including access to rapid tests, is a World Health Organization (WHO) strategy to accelerate progress toward ending TB. Faster and more sensitive molecular tests capable of diagnosing TB and drug-resistant TB have the technical capacity to address limitations associated with smears and cultures by increasing accuracy and shortening turnaround times as compared with those of these conventional laboratory methods. Nucleic acid amplification assays used to detect and analyze Mycobacterium tuberculosis (MTB)-complex nucleic acids can be used directly on specimens from patients suspected of having TB. Recently, several commercial molecular tests were developed to detect MTB and determine the drug resistance (DR) based on detection of specific genetic mutations conferring resistance. The first to be endorsed by the WHO was molecular line-probe assay technology. This test uses polymerase chain reaction (PCR) and reverse-hybridization methods to rapidly identify MTB and DR-related mutations simultaneously. More recently, the WHO endorsed Xpert MTB/RIF, Cepheid Inc, CA, USA, a fully automated assay used for TB diagnosis that relies upon PCR techniques for detection of TB and rifampicin resistance-related mutations. Other promising molecular TB assays for simplifying PCR-based testing protocols and increasing their accuracy are under development and evaluation. Although we lack a practical gold standard for the diagnosis of childhood TB, its bacteriological confirmation is always recommended to be sought whenever possible prior to a diagnostic decision being made. Conventional diagnostic laboratory TB tests are less efficient for children as compared with adults, because sufficient sputum samples are more difficult to collect from infants and young children, and their disease is often paucibacillary, resulting in smear-negative disease. These inherent challenges associated with childhood TB are due to immunological- and pathophysiological-response differences relative to those observed in adults. Several recent meta-analyses showed low sensitivity estimates of PCR-based TB assays for paucibacillary forms of TB (extrapulmonary TB and smear-negative pulmonary disease), which represent the vast majority of childhood TB cases. Despite the lack of evidence regarding use of the rapid molecular assays to identify TB and detect DR in children, and due to the clinical nature of childhood TB, TB-expert groups recommend including rapid methods for TB identification and DR detection in diagnostic algorithms for children suspected of both smear-positive and -negative pulmonary or extrapulmonary TB, both with or without human immunodeficiency virus (HIV)-coinfection, when combined with standard methods (including clinical, microbiological, and radiological assessment) for diagnosing active TB and conventional DR. Since 2011, the WHO has specifically recommended use of the Xpert MTB/RIF test as an initial diagnostic tool for children with suspected HIV-associated TB or multidrug-resistant TB based on successful treatment data related to adults. Implementation of the rapid molecular assays for rapid detection of TB and DR should occur in laboratories with proven capability to run molecular tests and where quality control systems are implemented. Molecular approaches should be more largely tested in children, given their status as the group in whom the diagnostic dilemma is most pronounced. These tests should also be included in specific childhood TB diagnostic algorithms adapted to the local/national context in combination with other strategies for improving diagnostics, including more effective specimen collection. Copyright © 2016.

Disk Crack Detection for Seeded Fault Engine Test

NASA Technical Reports Server (NTRS)

Luo, Huageng; Rodriguez, Hector; Hallman, Darren; Corbly, Dennis; Lewicki, David G. (Technical Monitor)

2004-01-01

Work was performed to develop and demonstrate vibration diagnostic techniques for the on-line detection of engine rotor disk cracks and other anomalies through a real engine test. An existing single-degree-of-freedom non-resonance-based vibration algorithm was extended to a multi-degree-of-freedom model. In addition, a resonance-based algorithm was also proposed for the case of one or more resonances. The algorithms were integrated into a diagnostic system using state-of-the- art commercial analysis equipment. The system required only non-rotating vibration signals, such as accelerometers and proximity probes, and the rotor shaft 1/rev signal to conduct the health monitoring. Before the engine test, the integrated system was tested in the laboratory by using a small rotor with controlled mass unbalances. The laboratory tests verified the system integration and both the non-resonance and the resonance-based algorithm implementations. In the engine test, the system concluded that after two weeks of cycling, the seeded fan disk flaw did not propagate to a large enough size to be detected by changes in the synchronous vibration. The unbalance induced by mass shifting during the start up and coast down was still the dominant response in the synchronous vibration.

Data Mining for Anomaly Detection

NASA Technical Reports Server (NTRS)

Biswas, Gautam; Mack, Daniel; Mylaraswamy, Dinkar; Bharadwaj, Raj

2013-01-01

The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

A Wave Diagnostics in Geophysics: Algorithmic Extraction of Atmosphere Disturbance Modes

NASA Astrophysics Data System (ADS)

Leble, S.; Vereshchagin, S.

2018-04-01

The problem of diagnostics in geophysics is discussed and a proposal based on dynamic projecting operators technique is formulated. The general exposition is demonstrated by an example of symbolic algorithm for the wave and entropy modes in the exponentially stratified atmosphere. The novel technique is developed as a discrete version for the evolution operator and the corresponding projectors via discrete Fourier transformation. Its explicit realization for directed modes in exponential one-dimensional atmosphere is presented via the correspondent projection operators in its discrete version in terms of matrices with a prescribed action on arrays formed from observation tables. A simulation based on opposite directed (upward and downward) wave train solution is performed and the modes' extraction from a mixture is illustrated.

A parallelizable real-time motion tracking algorithm with applications to ultrasonic strain imaging.

PubMed

Jiang, J; Hall, T J

2007-07-07

Ultrasound-based mechanical strain imaging systems utilize signals from conventional diagnostic ultrasound systems to image tissue elasticity contrast that provides new diagnostically valuable information. Previous works (Hall et al 2003 Ultrasound Med. Biol. 29 427, Zhu and Hall 2002 Ultrason. Imaging 24 161) demonstrated that uniaxial deformation with minimal elevation motion is preferred for breast strain imaging and real-time strain image feedback to operators is important to accomplish this goal. The work reported here enhances the real-time speckle tracking algorithm with two significant modifications. One fundamental change is that the proposed algorithm is a column-based algorithm (a column is defined by a line of data parallel to the ultrasound beam direction, i.e. an A-line), as opposed to a row-based algorithm (a row is defined by a line of data perpendicular to the ultrasound beam direction). Then, displacement estimates from its adjacent columns provide good guidance for motion tracking in a significantly reduced search region to reduce computational cost. Consequently, the process of displacement estimation can be naturally split into at least two separated tasks, computed in parallel, propagating outward from the center of the region of interest (ROI). The proposed algorithm has been implemented and optimized in a Windows system as a stand-alone ANSI C++ program. Results of preliminary tests, using numerical and tissue-mimicking phantoms, and in vivo tissue data, suggest that high contrast strain images can be consistently obtained with frame rates (10 frames s(-1)) that exceed our previous methods.

Near-infrared confocal micro-Raman spectroscopy combined with PCA-LDA multivariate analysis for detection of esophageal cancer

NASA Astrophysics Data System (ADS)

Chen, Long; Wang, Yue; Liu, Nenrong; Lin, Duo; Weng, Cuncheng; Zhang, Jixue; Zhu, Lihuan; Chen, Weisheng; Chen, Rong; Feng, Shangyuan

2013-06-01

The diagnostic capability of using tissue intrinsic micro-Raman signals to obtain biochemical information from human esophageal tissue is presented in this paper. Near-infrared micro-Raman spectroscopy combined with multivariate analysis was applied for discrimination of esophageal cancer tissue from normal tissue samples. Micro-Raman spectroscopy measurements were performed on 54 esophageal cancer tissues and 55 normal tissues in the 400-1750 cm-1 range. The mean Raman spectra showed significant differences between the two groups. Tentative assignments of the Raman bands in the measured tissue spectra suggested some changes in protein structure, a decrease in the relative amount of lactose, and increases in the percentages of tryptophan, collagen and phenylalanine content in esophageal cancer tissue as compared to those of a normal subject. The diagnostic algorithms based on principal component analysis (PCA) and linear discriminate analysis (LDA) achieved a diagnostic sensitivity of 87.0% and specificity of 70.9% for separating cancer from normal esophageal tissue samples. The result demonstrated that near-infrared micro-Raman spectroscopy combined with PCA-LDA analysis could be an effective and sensitive tool for identification of esophageal cancer.

How does relaxing the algorithm for autism affect DSM-V prevalence rates?

PubMed

Matson, Johnny L; Hattier, Megan A; Williams, Lindsey W

2012-08-01

Although it is still unclear what causes autism spectrum disorders (ASDs), over time researchers and clinicians have become more precise with detecting and diagnosing ASD. Many diagnoses, however, are based on the criteria established within the Diagnostic and Statistical Manual of Mental Disorders (DSM); thus, any change in these diagnostic criteria can have a great effect upon children with ASD and their families. It is predicted that the prevalence of ASD diagnoses will dramatically decrease with the adoption of the proposed DSM-5 criteria in 2013. The aim of this current study was to inspect the changes in prevalence first using a diagnostic criteria set which was modified slightly from the DSM-5 criteria (Modified-1 criteria) and again using a set of criteria which was relaxed even a bit more (Modified-2 criteria). Modified-1 resulted in 33.77 % fewer toddlers being diagnosed with ASD compared to the DSM-IV, while Modified-2 resulted in only a 17.98 % decrease in ASD diagnoses. Children diagnosed with the DSM-5 criteria exhibited the greatest levels of autism symptomatology, but the Mod-1, Mod-2, and DSM-IV groups still demonstrated significant impairments. Implications of these findings are discussed.

Pre-test probability of obstructive coronary stenosis in patients undergoing coronary CT angiography: Comparative performance of the modified diamond-Forrester algorithm versus methods incorporating cardiovascular risk factors.

PubMed

Ferreira, António Miguel; Marques, Hugo; Tralhão, António; Santos, Miguel Borges; Santos, Ana Rita; Cardoso, Gonçalo; Dores, Hélder; Carvalho, Maria Salomé; Madeira, Sérgio; Machado, Francisco Pereira; Cardim, Nuno; de Araújo Gonçalves, Pedro

2016-11-01

Current guidelines recommend the use of the Modified Diamond-Forrester (MDF) method to assess the pre-test likelihood of obstructive coronary artery disease (CAD). We aimed to compare the performance of the MDF method with two contemporary algorithms derived from multicenter trials that additionally incorporate cardiovascular risk factors: the calculator-based 'CAD Consortium 2' method, and the integer-based CONFIRM score. We assessed 1069 consecutive patients without known CAD undergoing coronary CT angiography (CCTA) for stable chest pain. Obstructive CAD was defined as the presence of coronary stenosis ≥50% on 64-slice dual-source CT. The three methods were assessed for calibration, discrimination, net reclassification, and changes in proposed downstream testing based upon calculated pre-test likelihoods. The observed prevalence of obstructive CAD was 13.8% (n=147). Overestimations of the likelihood of obstructive CAD were 140.1%, 9.8%, and 18.8%, respectively, for the MDF, CAD Consortium 2 and CONFIRM methods. The CAD Consortium 2 showed greater discriminative power than the MDF method, with a C-statistic of 0.73 vs. 0.70 (p<0.001), while the CONFIRM score did not (C-statistic 0.71, p=0.492). Reclassification of pre-test likelihood using the 'CAD Consortium 2' or CONFIRM scores resulted in a net reclassification improvement of 0.19 and 0.18, respectively, which would change the diagnostic strategy in approximately half of the patients. Newer risk factor-encompassing models allow for a more precise estimation of pre-test probabilities of obstructive CAD than the guideline-recommended MDF method. Adoption of these scores may improve disease prediction and change the diagnostic pathway in a significant proportion of patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Radiation dose reduction for CT lung cancer screening using ASIR and MBIR: a phantom study

PubMed Central

Mathieu, Kelsey B.; Ai, Hua; Fox, Patricia S.; Godoy, Myrna Cobos Barco; Munden, Reginald F.; de Groot, Patricia M.

2014-01-01

The purpose of this study was to reduce the radiation dosage associated with computed tomography (CT) lung cancer screening while maintaining overall diagnostic image quality and definition of ground‐glass opacities (GGOs). A lung screening phantom and a multipurpose chest phantom were used to quantitatively assess the performance of two iterative image reconstruction algorithms (adaptive statistical iterative reconstruction (ASIR) and model‐based iterative reconstruction (MBIR)) used in conjunction with reduced tube currents relative to a standard clinical lung cancer screening protocol (51 effective mAs (3.9 mGy) and filtered back‐projection (FBP) reconstruction). To further assess the algorithms' performances, qualitative image analysis was conducted (in the form of a reader study) using the multipurpose chest phantom, which was implanted with GGOs of two densities. Our quantitative image analysis indicated that tube current, and thus radiation dose, could be reduced by 40% or 80% from ASIR or MBIR, respectively, compared with conventional FBP, while maintaining similar image noise magnitude and contrast‐to‐noise ratio. The qualitative portion of our study, which assessed reader preference, yielded similar results, indicating that dose could be reduced by 60% (to 20 effective mAs (1.6 mGy)) with either ASIR or MBIR, while maintaining GGO definition. Additionally, the readers' preferences (as indicated by their ratings) regarding overall image quality were equal or better (for a given dose) when using ASIR or MBIR, compared with FBP. In conclusion, combining ASIR or MBIR with reduced tube current may allow for lower doses while maintaining overall diagnostic image quality, as well as GGO definition, during CT lung cancer screening. PACS numbers: 87.57.Q‐, 87.57.nf PMID:24710436

Evaluation of the Criterion and Convergent Validity of the Diagnostic Interview for Social and Communication Disorders in Young and Low-Functioning Children

ERIC Educational Resources Information Center

Maljaars, Jarymke; Noens, Ilse; Scholte, Evert; van Berckelaer-Onnes, Ina

2012-01-01

The Diagnostic Interview for Social and Communication Disorders (DISCO; Wing, 2006) is a standardized, semi-structured and interviewer-based schedule for diagnosis of autism spectrum disorder (ASD). The objective of this study was to evaluate the criterion and convergent validity of the DISCO-11 ICD-10 algorithm in young and low-functioning…

Clinical and public health implications of acute and early HIV detection and treatment: a scoping review.

PubMed

Rutstein, Sarah E; Ananworanich, Jintanat; Fidler, Sarah; Johnson, Cheryl; Sanders, Eduard J; Sued, Omar; Saez-Cirion, Asier; Pilcher, Christopher D; Fraser, Christophe; Cohen, Myron S; Vitoria, Marco; Doherty, Meg; Tucker, Joseph D

2017-06-28

The unchanged global HIV incidence may be related to ignoring acute HIV infection (AHI). This scoping review examines diagnostic, clinical, and public health implications of identifying and treating persons with AHI. We searched PubMed, in addition to hand-review of key journals identifying research pertaining to AHI detection and treatment. We focused on the relative contribution of AHI to transmission and the diagnostic, clinical, and public health implications. We prioritized research from low- and middle-income countries (LMICs) published in the last fifteen years. Extensive AHI research and limited routine AHI detection and treatment have begun in LMIC. Diagnostic challenges include ease-of-use, suitability for application and distribution in LMIC, and throughput for high-volume testing. Risk score algorithms have been used in LMIC to screen for AHI among individuals with behavioural and clinical characteristics more often associated with AHI. However, algorithms have not been implemented outside research settings. From a clinical perspective, there are substantial immunological and virological benefits to identifying and treating persons with AHI - evading the irreversible damage to host immune systems and seeding of viral reservoirs that occurs during untreated acute infection. The therapeutic benefits require rapid initiation of antiretrovirals, a logistical challenge in the absence of point-of-care testing. From a public health perspective, AHI diagnosis and treatment is critical to: decrease transmission via viral load reduction and behavioural interventions; improve pre-exposure prophylaxis outcomes by avoiding treatment initiation for HIV-seronegative persons with AHI; and, enhance partner services via notification for persons recently exposed or likely transmitting. There are undeniable clinical and public health benefits to AHI detection and treatment, but also substantial diagnostic and logistical barriers to implementation and scale-up. Effective early ART initiation may be critical for HIV eradication efforts, but widespread use in LMIC requires simple and accurate diagnostic tools. Implementation research is critical to facilitate sustainable integration of AHI detection and treatment into existing health systems and will be essential for prospective evaluation of testing algorithms, point-of-care diagnostics, and efficacious and effective first-line regimens.

Clinical and public health implications of acute and early HIV detection and treatment: a scoping review

PubMed Central

Rutstein, Sarah E.; Ananworanich, Jintanat; Fidler, Sarah; Johnson, Cheryl; Sanders, Eduard J.; Sued, Omar; Saez-Cirion, Asier; Pilcher, Christopher D.; Fraser, Christophe; Cohen, Myron S.; Vitoria, Marco; Doherty, Meg; Tucker, Joseph D.

2017-01-01

Abstract Introduction: The unchanged global HIV incidence may be related to ignoring acute HIV infection (AHI). This scoping review examines diagnostic, clinical, and public health implications of identifying and treating persons with AHI. Methods: We searched PubMed, in addition to hand-review of key journals identifying research pertaining to AHI detection and treatment. We focused on the relative contribution of AHI to transmission and the diagnostic, clinical, and public health implications. We prioritized research from low- and middle-income countries (LMICs) published in the last fifteen years. Results and Discussion: Extensive AHI research and limited routine AHI detection and treatment have begun in LMIC. Diagnostic challenges include ease-of-use, suitability for application and distribution in LMIC, and throughput for high-volume testing. Risk score algorithms have been used in LMIC to screen for AHI among individuals with behavioural and clinical characteristics more often associated with AHI. However, algorithms have not been implemented outside research settings. From a clinical perspective, there are substantial immunological and virological benefits to identifying and treating persons with AHI – evading the irreversible damage to host immune systems and seeding of viral reservoirs that occurs during untreated acute infection. The therapeutic benefits require rapid initiation of antiretrovirals, a logistical challenge in the absence of point-of-care testing. From a public health perspective, AHI diagnosis and treatment is critical to: decrease transmission via viral load reduction and behavioural interventions; improve pre-exposure prophylaxis outcomes by avoiding treatment initiation for HIV-seronegative persons with AHI; and, enhance partner services via notification for persons recently exposed or likely transmitting. Conclusions: There are undeniable clinical and public health benefits to AHI detection and treatment, but also substantial diagnostic and logistical barriers to implementation and scale-up. Effective early ART initiation may be critical for HIV eradication efforts, but widespread use in LMIC requires simple and accurate diagnostic tools. Implementation research is critical to facilitate sustainable integration of AHI detection and treatment into existing health systems and will be essential for prospective evaluation of testing algorithms, point-of-care diagnostics, and efficacious and effective first-line regimens. PMID:28691435

The JET diagnostic fast central acquisition and trigger system (abstract)

NASA Astrophysics Data System (ADS)

Edwards, A. W.; Blackler, K.

1995-01-01

Most plasma physics diagnostics sample at a fixed frequency that is normally matched to available memory limits. This technique is not appropriate for long pulse machines such as JET where sampling frequencies of hundreds of kHz are required to diagnose very fast events. As a result of work using real-time event selection within the previous JET soft x-ray diagnostic, a single data acquisition and event triggering system for all suitable fast diagnostics, the fast central acquisition and trigger system (Fast CATS), has been developed for JET. The front-end analog-to-digital conversion (ADC) part samples all channels at 250 kHz, with a 100 kHz pass band and a stop band of 125 kHz. The back-end data collection system is based around Texas Instruments TMS320C40 microprocessors. Within this system, two levels of trigger algorithms are able to evaluate data. The first level typically analyzes data on a per diagnostic and individual channel basis. The second level looks at the data from one or more diagnostics in a window around the time of interest flagged by the first level system. Selection criteria defined by the diagnosticians are then imposed on the results from the second level to decide whether that data should be kept. The use of such a system involving intelligent real time trigger algorithms and fast data analysis will improve both the quantity and quality of JET diagnostic data, while providing valuable input to the design of data acquisition systems for very long pulse machines such as ITER. This paper will give an overview of the various elements of this new system. In addition, first results from this system following the restart of JET operation will be presented.

Normal pressure hydrocephalus: survey on contemporary diagnostic algorithms and therapeutic decision-making in clinical practice.

PubMed

Krauss, J K; Halve, B

2004-04-01

There is no agreement on the best diagnostic criteria for selecting patients with normal pressure hydrocephalus (NPH) for CSF shunting. The primary objective of the present study was to provide a contemporary survey on diagnostic algorithms and therapeutic decision-making in clinical practice. The secondary objective was to estimate the incidence of NPH. Standardized questionnaires with sections on the incidence of NPH and the frequency of shunting, evaluation of clinical symptoms, and signs, diagnostic studies, therapeutic decision-making and operative techniques, postoperative outcome and complications, and the profiles of different centers, were sent to 82 neurosurgical centers in Germany known to participate in the care of patients with NPH. Data were analyzed from 49 of 53 centers which responded to the survey (65%). The estimated annual incidence of NPH was 1.8 cases/100.000 inhabitants. Gait disturbance was defined as the most important sign of NPH (61%). There was a wide variety in the choice of diagnostic tests. Cisternography was performed routinely only in single centers. Diagnostic CSF removal was used with varying frequency by all centers except one, but the amount of CSF removed by lumbar puncture differed markedly between centers. There was poor agreement on criteria for evaluation of continuous intracranial pressure recordings regarding both the amplitude and the relative frequency of B-waves. Both periventricular and deep white matter lesions were present in about 50% of patients being shunted, indicating that vascular comorbidity in NPH patients has gained more acceptance. Programmable shunts were used by more than half of the centers, and newer valve types such as gravitational valves have become more popular. According to the present survey, new diagnostic and therapeutic concepts on NPH have penetrated daily routine to a certain extent. Wide variability, however, still exists among different neurosurgical centers.

[A diagnostic algorithm and treatment procedure in disordered vital functions in newborns admitted to a resuscitation ward].

PubMed

Ostreĭkov, I F; Podkopaev, V N; Moiseev, D B; Karpysheva, E V; Markova, L A; Sizov, S V

1997-01-01

Total mortality decreased by 2.5 times in the wards for intensive care of the newborns in the Tushino Pediatric Hospital in 1996 and is now 7.6%. Such results are due to a complex of measures, one such measure being the development and introduction of an algorithm for the diagnosis and treatment of newborns hospitalized in intensive care wards. The algorithm facilitates the work of the staff, helps earlier diagnose a disease, and, hence, carry out timely scientifically based therapy.

Inverse scattering and refraction corrected reflection for breast cancer imaging

NASA Astrophysics Data System (ADS)

Wiskin, J.; Borup, D.; Johnson, S.; Berggren, M.; Robinson, D.; Smith, J.; Chen, J.; Parisky, Y.; Klock, John

2010-03-01

Reflection ultrasound (US) has been utilized as an adjunct imaging modality for over 30 years. TechniScan, Inc. has developed unique, transmission and concomitant reflection algorithms which are used to reconstruct images from data gathered during a tomographic breast scanning process called Warm Bath Ultrasound (WBU™). The transmission algorithm yields high resolution, 3D, attenuation and speed of sound (SOS) images. The reflection algorithm is based on canonical ray tracing utilizing refraction correction via the SOS and attenuation reconstructions. The refraction correction reflection algorithm allows 360 degree compounding resulting in the reflection image. The requisite data are collected when scanning the entire breast in a 33° C water bath, on average in 8 minutes. This presentation explains how the data are collected and processed by the 3D transmission and reflection imaging mode algorithms. The processing is carried out using two NVIDIA® Tesla™ GPU processors, accessing data on a 4-TeraByte RAID. The WBU™ images are displayed in a DICOM viewer that allows registration of all three modalities. Several representative cases are presented to demonstrate potential diagnostic capability including: a cyst, fibroadenoma, and a carcinoma. WBU™ images (SOS, attenuation, and reflection modalities) are shown along with their respective mammograms and standard ultrasound images. In addition, anatomical studies are shown comparing WBU™ images and MRI images of a cadaver breast. This innovative technology is designed to provide additional tools in the armamentarium for diagnosis of breast disease.

Emergency ultrasound-based algorithms for diagnosing blunt abdominal trauma.

PubMed

Stengel, Dirk; Bauwens, Kai; Rademacher, Grit; Ekkernkamp, Axel; Güthoff, Claas

2013-07-31

Ultrasonography is regarded as the tool of choice for early diagnostic investigations in patients with suspected blunt abdominal trauma. Although its sensitivity is too low for definite exclusion of abdominal organ injury, proponents of ultrasound argue that ultrasound-based clinical pathways enhance the speed of primary trauma assessment, reduce the number of computed tomography scans and cut costs. To assess the effects of trauma algorithms that include ultrasound examinations in patients with suspected blunt abdominal trauma. We searched the Cochrane Injuries Group's Specialised Register, CENTRAL (The Cochrane Library), MEDLINE (OvidSP), EMBASE (OvidSP), CINAHL (EBSCO), publishers' databases, controlled trials registers and the Internet. Bibliographies of identified articles and conference abstracts were searched for further elligible studies. Trial authors were contacted for further information and individual patient data. The searches were updated in February 2013. randomised controlled trials (RCTs) and quasi-randomised trials (qRCTs). patients with blunt torso, abdominal or multiple trauma undergoing diagnostic investigations for abdominal organ injury. diagnostic algorithms comprising emergency ultrasonography (US). diagnostic algorithms without ultrasound examinations (for example, primary computed tomography [CT] or diagnostic peritoneal lavage [DPL]). mortality, use of CT and DPL, cost-effectiveness, laparotomy and negative laparotomy rates, delayed diagnoses, and quality of life. Two authors independently selected trials for inclusion, assessed methodological quality and extracted data. Where possible, data were pooled and relative risks (RRs), risk differences (RDs) and weighted mean differences, each with 95% confidence intervals (CIs), were calculated by fixed- or random-effects modelling, as appropriate. We identified four studies meeting our inclusion criteria. Overall, trials were of moderate methodological quality. Few trial authors responded to our written inquiries seeking to resolve controversial issues and to obtain individual patient data. We pooled mortality data from three trials involving 1254 patients; relative risk in favour of the US arm was 1.00 (95% CI 0.50 to 2.00). US-based pathways significantly reduced the number of CT scans (random-effects RD -0.52, 95% CI -0.83 to -0.21), but the meaning of this result is unclear. Given the low sensitivity of ultrasound, the reduction in CT scans may either translate to a number needed to treat or number needed to harm of two. There is currently insufficient evidence from RCTs to justify promotion of ultrasound-based clinical pathways in diagnosing patients with suspected blunt abdominal trauma.

Direct targeting of risk factors significantly increases the detection of liver cirrhosis in primary care: a cross-sectional diagnostic study utilising transient elastography.

PubMed

Harman, David J; Ryder, Stephen D; James, Martin W; Jelpke, Matthew; Ottey, Dominic S; Wilkes, Emilie A; Card, Timothy R; Aithal, Guruprasad P; Guha, Indra Neil

2015-05-03

To assess the feasibility of a novel diagnostic algorithm targeting patients with risk factors for chronic liver disease in a community setting. Prospective cross-sectional study. Two primary care practices (adult patient population 10,479) in Nottingham, UK. Adult patients (aged 18 years or over) fulfilling one or more selected risk factors for developing chronic liver disease: (1) hazardous alcohol use, (2) type 2 diabetes or (3) persistently elevated alanine aminotransferase (ALT) liver function enzyme with negative serology. A serial biomarker algorithm, using a simple blood-based marker (aspartate aminotransferase:ALT ratio for hazardous alcohol users, BARD score for other risk groups) and subsequently liver stiffness measurement using transient elastography (TE). Diagnosis of clinically significant liver disease (defined as liver stiffness ≥8 kPa); definitive diagnosis of liver cirrhosis. We identified 920 patients with the defined risk factors of whom 504 patients agreed to undergo investigation. A normal blood biomarker was found in 62 patients (12.3%) who required no further investigation. Subsequently, 378 patients agreed to undergo TE, of whom 98 (26.8% of valid scans) had elevated liver stiffness. Importantly, 71/98 (72.4%) patients with elevated liver stiffness had normal liver enzymes and would be missed by traditional investigation algorithms. We identified 11 new patients with definite cirrhosis, representing a 140% increase in the number of diagnosed cases in this population. A non-invasive liver investigation algorithm based in a community setting is feasible to implement. Targeting risk factors using a non-invasive biomarker approach identified a substantial number of patients with previously undetected cirrhosis. The diagnostic algorithm utilised for this study can be found on clinicaltrials.gov (NCT02037867), and is part of a continuing longitudinal cohort study. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Retrieved Vertical Profiles of Latent Heat Release Using TRMM Rainfall Products

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Olson, W. S.; Meneghini, R.; Yang, S.; Simpson, J.; Kummerow, C.; Smith, E.

2000-01-01

This paper represents the first attempt to use TRMM rainfall information to estimate the four dimensional latent heating structure over the global tropics for February 1998. The mean latent heating profiles over six oceanic regions (TOGA COARE IFA, Central Pacific, S. Pacific Convergence Zone, East Pacific, Indian Ocean and Atlantic Ocean) and three continental regions (S. America, Central Africa and Australia) are estimated and studied. The heating profiles obtained from the results of diagnostic budget studies over a broad range of geographic locations are used to provide comparisons and indirect validation for the heating algorithm estimated heating profiles. Three different latent heating algorithms, the Goddard Convective-Stratiform (CSH) heating, the Goddard Profiling (GPROF) heating, and the Hydrometeor heating (HH) are used and their results are intercompared. The horizontal distribution or patterns of latent heat release from the three different heating retrieval methods are quite similar. They all can identify the areas of major convective activity (i.e., a well defined ITCZ in the Pacific, a distinct SPCZ) in the global tropics. The magnitude of their estimated latent heating release is also not in bad agreement with each other and with those determined from diagnostic budget studies. However, the major difference among these three heating retrieval algorithms is the altitude of the maximum heating level. The CSH algorithm estimated heating profiles only show one maximum heating level, and the level varies between convective activity from various geographic locations. These features are in good agreement with diagnostic budget studies. By contrast, two maximum heating levels were found using the GPROF heating and HH algorithms. The latent heating profiles estimated from all three methods can not show cooling between active convective events. We also examined the impact of different TMI (Multi-channel Passive Microwave Sensor) and PR (Precipitation Radar) rainfall information on latent heating structures.

Infectious encephalitis: utility of a rational approach to aetiological diagnosis in daily clinical practice.

PubMed

López-Sánchez, C; Sulleiro, E; Bocanegra, C; Romero, S; Codina, G; Sanz, I; Esperalba, J; Serra, J; Pigrau, C; Burgos, J; Almirante, B; Falcó, V

2017-04-01

In this study we attempt to assess the utility of a simplified step-wise diagnostic algorithm to determinate the aetiology of encephalitis in daily clinical practice and to describe the main causes in our setting. This was a prospective cohort study of all consecutive cases of encephalitis in adult patients diagnosed between January 2010 and March 2015 at the University Hospital Vall d'Hebron in Barcelona, Spain. The aetiological study was carried out following the proposed step-wise algorithm. The proportion of aetiological diagnoses achieved in each step was analysed. Data from 97 patients with encephalitis were assessed. Following a simplified step-wise algorithm, a definite diagnosis was made in the first step in 53 patients (55 %) and in 12 additional cases (12 %) in the second step. Overall, a definite or probable aetiological diagnosis was achieved in 78 % of the cases. Herpes virus, L. monocytogenes and M. tuberculosis were the leading causative agents demonstrated, whereas less frequent aetiologies were observed, mainly in immunosuppressed patients. The overall related mortality was 13.4 %. According to our experience, the leading and treatable causes of encephalitis can be identified in a first diagnostic step with limited microbiological studies. L. monocytogenes treatment should be considered on arrival in some patients. Additional diagnostic effort should be made in immunosuppressed patients.

Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

NASA Astrophysics Data System (ADS)

Georgiou, Harris

2009-10-01

Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.

Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

2007-03-01

Multislice CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multislice CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. Moreover, we have provided diagnostic assistance methods to medical screening specialists by using a lung cancer screening algorithm built into mobile helical CT scanner for the lung cancer mass screening done in the region without the hospital. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system.

An algorithm for identification and classification of individuals with type 1 and type 2 diabetes mellitus in a large primary care database

PubMed Central

Sharma, Manuj; Petersen, Irene; Nazareth, Irwin; Coton, Sonia J

2016-01-01

Background Research into diabetes mellitus (DM) often requires a reproducible method for identifying and distinguishing individuals with type 1 DM (T1DM) and type 2 DM (T2DM). Objectives To develop a method to identify individuals with T1DM and T2DM using UK primary care electronic health records. Methods Using data from The Health Improvement Network primary care database, we developed a two-step algorithm. The first algorithm step identified individuals with potential T1DM or T2DM based on diagnostic records, treatment, and clinical test results. We excluded individuals with records for rarer DM subtypes only. For individuals to be considered diabetic, they needed to have at least two records indicative of DM; one of which was required to be a diagnostic record. We then classified individuals with T1DM and T2DM using the second algorithm step. A combination of diagnostic codes, medication prescribed, age at diagnosis, and whether the case was incident or prevalent were used in this process. We internally validated this classification algorithm through comparison against an independent clinical examination of The Health Improvement Network electronic health records for a random sample of 500 DM individuals. Results Out of 9,161,866 individuals aged 0–99 years from 2000 to 2014, we classified 37,693 individuals with T1DM and 418,433 with T2DM, while 1,792 individuals remained unclassified. A small proportion were classified with some uncertainty (1,155 [3.1%] of all individuals with T1DM and 6,139 [1.5%] with T2DM) due to unclear health records. During validation, manual assignment of DM type based on clinical assessment of the entire electronic record and algorithmic assignment led to equivalent classification in all instances. Conclusion The majority of individuals with T1DM and T2DM can be readily identified from UK primary care electronic health records. Our approach can be adapted for use in other health care settings. PMID:27785102

An algorithm for identification and classification of individuals with type 1 and type 2 diabetes mellitus in a large primary care database.

PubMed

Sharma, Manuj; Petersen, Irene; Nazareth, Irwin; Coton, Sonia J

2016-01-01

Research into diabetes mellitus (DM) often requires a reproducible method for identifying and distinguishing individuals with type 1 DM (T1DM) and type 2 DM (T2DM). To develop a method to identify individuals with T1DM and T2DM using UK primary care electronic health records. Using data from The Health Improvement Network primary care database, we developed a two-step algorithm. The first algorithm step identified individuals with potential T1DM or T2DM based on diagnostic records, treatment, and clinical test results. We excluded individuals with records for rarer DM subtypes only. For individuals to be considered diabetic, they needed to have at least two records indicative of DM; one of which was required to be a diagnostic record. We then classified individuals with T1DM and T2DM using the second algorithm step. A combination of diagnostic codes, medication prescribed, age at diagnosis, and whether the case was incident or prevalent were used in this process. We internally validated this classification algorithm through comparison against an independent clinical examination of The Health Improvement Network electronic health records for a random sample of 500 DM individuals. Out of 9,161,866 individuals aged 0-99 years from 2000 to 2014, we classified 37,693 individuals with T1DM and 418,433 with T2DM, while 1,792 individuals remained unclassified. A small proportion were classified with some uncertainty (1,155 [3.1%] of all individuals with T1DM and 6,139 [1.5%] with T2DM) due to unclear health records. During validation, manual assignment of DM type based on clinical assessment of the entire electronic record and algorithmic assignment led to equivalent classification in all instances. The majority of individuals with T1DM and T2DM can be readily identified from UK primary care electronic health records. Our approach can be adapted for use in other health care settings.

The Usefulness of Rapid Diagnostic Tests in the New Context of Low Malaria Transmission in Zanzibar

PubMed Central

Shakely, Delér; Msellem, Mwinyi I.; Morris, Ulrika; Omar, Rahila; Weiping, Xu; Petzold, Max; Greenhouse, Bryan; Baltzell, Kimberly A.; Ali, Abdullah S.; Björkman, Anders; Mårtensson, Andreas

2013-01-01

Background We assessed if histidine-rich-protein-2 (HRP2) based rapid diagnostic test (RDT) remains an efficient tool for Plasmodium falciparum case detection among fever patients in Zanzibar and if primary health care workers continue to adhere to RDT results in the new epidemiological context of low malaria transmission. Further, we evaluated the performance of RDT within the newly adopted integrated management of childhood illness (IMCI) algorithm in Zanzibar. Methods and Findings We enrolled 3890 patients aged ≥2 months with uncomplicated febrile illness in this health facility based observational study conducted in 12 primary health care facilities in Zanzibar, between May-July 2010. One patient had an inconclusive RDT result. Overall 121/3889 (3.1%) patients were RDT positive. The highest RDT positivity rate, 32/528 (6.1%), was found in children aged 5–14 years. RDT sensitivity and specificity against PCR was 76.5% (95% CI 69.0–83.9%) and 99.9% (95% CI 99.7–100%), and against blood smear microscopy 78.6% (95% CI 70.8–85.1%) and 99.7% (95% CI 99.6–99.9%), respectively. All RDT positive, but only 3/3768 RDT negative patients received anti-malarial treatment. Adherence to RDT results was thus 3887/3889 (99.9%). RDT performed well in the IMCI algorithm with equally high adherence among children <5 years as compared with other age groups. Conclusions The sensitivity of HRP-2 based RDT in the hands of health care workers compared with both PCR and microscopy for P. falciparum case detection was relatively low, whereas adherence to test results with anti-malarial treatment was excellent. Moreover, the results provide evidence that RDT can be reliably integrated in IMCI as a tool for improved childhood fever management. However, the relatively low RDT sensitivity highlights the need for improved quality control of RDT use in primary health care facilities, but also for more sensitive point-of-care malaria diagnostic tools in the new epidemiological context of low malaria transmission in Zanzibar. Trial registration ClinicalTrials.gov NCT01002066 PMID:24023791

Behavior of optical properties of coagulated blood sample at 633 nm wavelength

NASA Astrophysics Data System (ADS)

Morales Cruzado, Beatriz; Vázquez y Montiel, Sergio; Delgado Atencio, José Alberto

2011-03-01

Determination of tissue optical parameters is fundamental for application of light in either diagnostics or therapeutical procedures. However, in samples of biological tissue in vitro, the optical properties are modified by cellular death or cellular agglomeration that can not be avoided. This phenomena change the propagation of light within the biological sample. Optical properties of human blood tissue were investigated in vitro at 633 nm using an optical setup that includes a double integrating sphere system. We measure the diffuse transmittance and diffuse reflectance of the blood sample and compare these physical properties with those obtained by Monte Carlo Multi-Layered (MCML). The extraction of the optical parameters: absorption coefficient μa, scattering coefficient μs and anisotropic factor g from the measurements were carried out using a Genetic Algorithm, in which the search procedure is based in the evolution of a population due to selection of the best individual, evaluated by a function that compares the diffuse transmittance and diffuse reflectance of those individuals with the experimental ones. The algorithm converges rapidly to the best individual, extracting the optical parameters of the sample. We compare our results with those obtained by using other retrieve procedures. We found that the scattering coefficient and the anisotropic factor change dramatically due to the formation of clusters.

The Diagnosis of Urinary Tract infection in Young children (DUTY): a diagnostic prospective observational study to derive and validate a clinical algorithm for the diagnosis of urinary tract infection in children presenting to primary care with an acute illness.

PubMed Central

Hay, Alastair D; Birnie, Kate; Busby, John; Delaney, Brendan; Downing, Harriet; Dudley, Jan; Durbaba, Stevo; Fletcher, Margaret; Harman, Kim; Hollingworth, William; Hood, Kerenza; Howe, Robin; Lawton, Michael; Lisles, Catherine; Little, Paul; MacGowan, Alasdair; O'Brien, Kathryn; Pickles, Timothy; Rumsby, Kate; Sterne, Jonathan Ac; Thomas-Jones, Emma; van der Voort, Judith; Waldron, Cherry-Ann; Whiting, Penny; Wootton, Mandy; Butler, Christopher C

2016-01-01

BACKGROUND It is not clear which young children presenting acutely unwell to primary care should be investigated for urinary tract infection (UTI) and whether or not dipstick testing should be used to inform antibiotic treatment. OBJECTIVES To develop algorithms to accurately identify pre-school children in whom urine should be obtained; assess whether or not dipstick urinalysis provides additional diagnostic information; and model algorithm cost-effectiveness. DESIGN Multicentre, prospective diagnostic cohort study. SETTING AND PARTICIPANTS Children < 5 years old presenting to primary care with an acute illness and/or new urinary symptoms. METHODS One hundred and seven clinical characteristics (index tests) were recorded from the child's past medical history, symptoms, physical examination signs and urine dipstick test. Prior to dipstick results clinician opinion of UTI likelihood ('clinical diagnosis') and urine sampling and treatment intentions ('clinical judgement') were recorded. All index tests were measured blind to the reference standard, defined as a pure or predominant uropathogen cultured at ≥ 10(5) colony-forming units (CFU)/ml in a single research laboratory. Urine was collected by clean catch (preferred) or nappy pad. Index tests were sequentially evaluated in two groups, stratified by urine collection method: parent-reported symptoms with clinician-reported signs, and urine dipstick results. Diagnostic accuracy was quantified using area under receiver operating characteristic curve (AUROC) with 95% confidence interval (CI) and bootstrap-validated AUROC, and compared with the 'clinician diagnosis' AUROC. Decision-analytic models were used to identify optimal urine sampling strategy compared with 'clinical judgement'. RESULTS A total of 7163 children were recruited, of whom 50% were female and 49% were < 2 years old. Culture results were available for 5017 (70%); 2740 children provided clean-catch samples, 94% of whom were ≥ 2 years old, with 2.2% meeting the UTI definition. Among these, 'clinical diagnosis' correctly identified 46.6% of positive cultures, with 94.7% specificity and an AUROC of 0.77 (95% CI 0.71 to 0.83). Four symptoms, three signs and three dipstick results were independently associated with UTI with an AUROC (95% CI; bootstrap-validated AUROC) of 0.89 (0.85 to 0.95; validated 0.88) for symptoms and signs, increasing to 0.93 (0.90 to 0.97; validated 0.90) with dipstick results. Nappy pad samples were provided from the other 2277 children, of whom 82% were < 2 years old and 1.3% met the UTI definition. 'Clinical diagnosis' correctly identified 13.3% positive cultures, with 98.5% specificity and an AUROC of 0.63 (95% CI 0.53 to 0.72). Four symptoms and two dipstick results were independently associated with UTI, with an AUROC of 0.81 (0.72 to 0.90; validated 0.78) for symptoms, increasing to 0.87 (0.80 to 0.94; validated 0.82) with the dipstick findings. A high specificity threshold for the clean-catch model was more accurate and less costly than, and as effective as, clinical judgement. The additional diagnostic utility of dipstick testing was offset by its costs. The cost-effectiveness of the nappy pad model was not clear-cut. CONCLUSIONS Clinicians should prioritise the use of clean-catch sampling as symptoms and signs can cost-effectively improve the identification of UTI in young children where clean catch is possible. Dipstick testing can improve targeting of antibiotic treatment, but at a higher cost than waiting for a laboratory result. Future research is needed to distinguish pathogens from contaminants, assess the impact of the clean-catch algorithm on patient outcomes, and the cost-effectiveness of presumptive versus dipstick versus laboratory-guided antibiotic treatment. FUNDING The National Institute for Health Research Health Technology Assessment programme. PMID:27401902

The Diagnosis of Urinary Tract infection in Young children (DUTY): a diagnostic prospective observational study to derive and validate a clinical algorithm for the diagnosis of urinary tract infection in children presenting to primary care with an acute illness.

PubMed

Hay, Alastair D; Birnie, Kate; Busby, John; Delaney, Brendan; Downing, Harriet; Dudley, Jan; Durbaba, Stevo; Fletcher, Margaret; Harman, Kim; Hollingworth, William; Hood, Kerenza; Howe, Robin; Lawton, Michael; Lisles, Catherine; Little, Paul; MacGowan, Alasdair; O'Brien, Kathryn; Pickles, Timothy; Rumsby, Kate; Sterne, Jonathan Ac; Thomas-Jones, Emma; van der Voort, Judith; Waldron, Cherry-Ann; Whiting, Penny; Wootton, Mandy; Butler, Christopher C

2016-07-01

It is not clear which young children presenting acutely unwell to primary care should be investigated for urinary tract infection (UTI) and whether or not dipstick testing should be used to inform antibiotic treatment. To develop algorithms to accurately identify pre-school children in whom urine should be obtained; assess whether or not dipstick urinalysis provides additional diagnostic information; and model algorithm cost-effectiveness. Multicentre, prospective diagnostic cohort study. Children < 5 years old presenting to primary care with an acute illness and/or new urinary symptoms. One hundred and seven clinical characteristics (index tests) were recorded from the child's past medical history, symptoms, physical examination signs and urine dipstick test. Prior to dipstick results clinician opinion of UTI likelihood ('clinical diagnosis') and urine sampling and treatment intentions ('clinical judgement') were recorded. All index tests were measured blind to the reference standard, defined as a pure or predominant uropathogen cultured at ≥ 10(5) colony-forming units (CFU)/ml in a single research laboratory. Urine was collected by clean catch (preferred) or nappy pad. Index tests were sequentially evaluated in two groups, stratified by urine collection method: parent-reported symptoms with clinician-reported signs, and urine dipstick results. Diagnostic accuracy was quantified using area under receiver operating characteristic curve (AUROC) with 95% confidence interval (CI) and bootstrap-validated AUROC, and compared with the 'clinician diagnosis' AUROC. Decision-analytic models were used to identify optimal urine sampling strategy compared with 'clinical judgement'. A total of 7163 children were recruited, of whom 50% were female and 49% were < 2 years old. Culture results were available for 5017 (70%); 2740 children provided clean-catch samples, 94% of whom were ≥ 2 years old, with 2.2% meeting the UTI definition. Among these, 'clinical diagnosis' correctly identified 46.6% of positive cultures, with 94.7% specificity and an AUROC of 0.77 (95% CI 0.71 to 0.83). Four symptoms, three signs and three dipstick results were independently associated with UTI with an AUROC (95% CI; bootstrap-validated AUROC) of 0.89 (0.85 to 0.95; validated 0.88) for symptoms and signs, increasing to 0.93 (0.90 to 0.97; validated 0.90) with dipstick results. Nappy pad samples were provided from the other 2277 children, of whom 82% were < 2 years old and 1.3% met the UTI definition. 'Clinical diagnosis' correctly identified 13.3% positive cultures, with 98.5% specificity and an AUROC of 0.63 (95% CI 0.53 to 0.72). Four symptoms and two dipstick results were independently associated with UTI, with an AUROC of 0.81 (0.72 to 0.90; validated 0.78) for symptoms, increasing to 0.87 (0.80 to 0.94; validated 0.82) with the dipstick findings. A high specificity threshold for the clean-catch model was more accurate and less costly than, and as effective as, clinical judgement. The additional diagnostic utility of dipstick testing was offset by its costs. The cost-effectiveness of the nappy pad model was not clear-cut. Clinicians should prioritise the use of clean-catch sampling as symptoms and signs can cost-effectively improve the identification of UTI in young children where clean catch is possible. Dipstick testing can improve targeting of antibiotic treatment, but at a higher cost than waiting for a laboratory result. Future research is needed to distinguish pathogens from contaminants, assess the impact of the clean-catch algorithm on patient outcomes, and the cost-effectiveness of presumptive versus dipstick versus laboratory-guided antibiotic treatment. The National Institute for Health Research Health Technology Assessment programme.

Drug hypersensitivity in children: report from the pediatric task force of the EAACI Drug Allergy Interest Group.

PubMed

Gomes, E R; Brockow, K; Kuyucu, S; Saretta, F; Mori, F; Blanca-Lopez, N; Ott, H; Atanaskovic-Markovic, M; Kidon, M; Caubet, J-C; Terreehorst, I

2016-02-01

When questioned, about 10% of the parents report suspected hypersensitivity to at least one drug in their children. However, only a few of these reactions can be confirmed as allergic after a diagnostic workup. There is still a lack of knowledge on drug hypersensitivity (DH) epidemiology, clinical spectrum, and appropriate diagnostic methods particularly in children. Meanwhile, the tools used for DH management in adults are applied also for children. Whereas this appears generally acceptable, some aspects of DH and management differ with age. Most reactions in children are still attributed to betalactams. Some manifestations, such as nonsteroidal anti-inflammatory drug-associated angioedema and serum sickness-like reactions, are more frequent among young patients as compared to adults. Risk factors such as viral infections are particularly frequent in children, making the diagnosis challenging. The practicability and validity of skin test and other diagnostic procedures need further assessment in children. This study presents an up-to-date review on epidemiology, clinical spectrum, diagnostic tools, and current management of DH in children. A new general algorithm for the study of these reactions in children is proposed. Data are presented focusing on reported differences between pediatric and adult patients, also identifying unmet needs to be addressed in further research. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Specificity and Sensitivity of Claims-Based Algorithms for Identifying Members of Medicare+Choice Health Plans That Have Chronic Medical Conditions

PubMed Central

Rector, Thomas S; Wickstrom, Steven L; Shah, Mona; Thomas Greeenlee, N; Rheault, Paula; Rogowski, Jeannette; Freedman, Vicki; Adams, John; Escarce, José J

2004-01-01

Objective To examine the effects of varying diagnostic and pharmaceutical criteria on the performance of claims-based algorithms for identifying beneficiaries with hypertension, heart failure, chronic lung disease, arthritis, glaucoma, and diabetes. Study Setting Secondary 1999–2000 data from two Medicare+Choice health plans. Study Design Retrospective analysis of algorithm specificity and sensitivity. Data Collection Physician, facility, and pharmacy claims data were extracted from electronic records for a sample of 3,633 continuously enrolled beneficiaries who responded to an independent survey that included questions about chronic diseases. Principal Findings Compared to an algorithm that required a single medical claim in a one-year period that listed the diagnosis, either requiring that the diagnosis be listed on two separate claims or that the diagnosis to be listed on one claim for a face-to-face encounter with a health care provider significantly increased specificity for the conditions studied by 0.03 to 0.11. Specificity of algorithms was significantly improved by 0.03 to 0.17 when both a medical claim with a diagnosis and a pharmacy claim for a medication commonly used to treat the condition were required. Sensitivity improved significantly by 0.01 to 0.20 when the algorithm relied on a medical claim with a diagnosis or a pharmacy claim, and by 0.05 to 0.17 when two years rather than one year of claims data were analyzed. Algorithms that had specificity more than 0.95 were found for all six conditions. Sensitivity above 0.90 was not achieved all conditions. Conclusions Varying claims criteria improved the performance of case-finding algorithms for six chronic conditions. Highly specific, and sometimes sensitive, algorithms for identifying members of health plans with several chronic conditions can be developed using claims data. PMID:15533190

Retinal Nerve Fiber Layer Segmentation on FD-OCT Scans of Normal Subjects and Glaucoma Patients.

PubMed

Mayer, Markus A; Hornegger, Joachim; Mardin, Christian Y; Tornow, Ralf P

2010-11-08

Automated measurements of the retinal nerve fiber layer thickness on circular OCT B-Scans provide physicians additional parameters for glaucoma diagnosis. We propose a novel retinal nerve fiber layer segmentation algorithm for frequency domain data that can be applied on scans from both normal healthy subjects, as well as glaucoma patients, using the same set of parameters. In addition, the algorithm remains almost unaffected by image quality. The main part of the segmentation process is based on the minimization of an energy function consisting of gradient and local smoothing terms. A quantitative evaluation comparing the automated segmentation results to manually corrected segmentations from three reviewers is performed. A total of 72 scans from glaucoma patients and 132 scans from normal subjects, all from different persons, composed the database for the evaluation of the segmentation algorithm. A mean absolute error per A-Scan of 2.9 µm was achieved on glaucomatous eyes, and 3.6 µm on healthy eyes. The mean absolute segmentation error over all A-Scans lies below 10 µm on 95.1% of the images. Thus our approach provides a reliable tool for extracting diagnostic relevant parameters from OCT B-Scans for glaucoma diagnosis.

Retinal Nerve Fiber Layer Segmentation on FD-OCT Scans of Normal Subjects and Glaucoma Patients

PubMed Central

Mayer, Markus A.; Hornegger, Joachim; Mardin, Christian Y.; Tornow, Ralf P.

2010-01-01

Automated measurements of the retinal nerve fiber layer thickness on circular OCT B-Scans provide physicians additional parameters for glaucoma diagnosis. We propose a novel retinal nerve fiber layer segmentation algorithm for frequency domain data that can be applied on scans from both normal healthy subjects, as well as glaucoma patients, using the same set of parameters. In addition, the algorithm remains almost unaffected by image quality. The main part of the segmentation process is based on the minimization of an energy function consisting of gradient and local smoothing terms. A quantitative evaluation comparing the automated segmentation results to manually corrected segmentations from three reviewers is performed. A total of 72 scans from glaucoma patients and 132 scans from normal subjects, all from different persons, composed the database for the evaluation of the segmentation algorithm. A mean absolute error per A-Scan of 2.9 µm was achieved on glaucomatous eyes, and 3.6 µm on healthy eyes. The mean absolute segmentation error over all A-Scans lies below 10 µm on 95.1% of the images. Thus our approach provides a reliable tool for extracting diagnostic relevant parameters from OCT B-Scans for glaucoma diagnosis. PMID:21258556

A statistical approach to detection of copy number variations in PCR-enriched targeted sequencing data.

PubMed

Demidov, German; Simakova, Tamara; Vnuchkova, Julia; Bragin, Anton

2016-10-22

Multiplex polymerase chain reaction (PCR) is a common enrichment technique for targeted massive parallel sequencing (MPS) protocols. MPS is widely used in biomedical research and clinical diagnostics as the fast and accurate tool for the detection of short genetic variations. However, identification of larger variations such as structure variants and copy number variations (CNV) is still being a challenge for targeted MPS. Some approaches and tools for structural variants detection were proposed, but they have limitations and often require datasets of certain type, size and expected number of amplicons affected by CNVs. In the paper, we describe novel algorithm for high-resolution germinal CNV detection in the PCR-enriched targeted sequencing data and present accompanying tool. We have developed a machine learning algorithm for the detection of large duplications and deletions in the targeted sequencing data generated with PCR-based enrichment step. We have performed verification studies and established the algorithm's sensitivity and specificity. We have compared developed tool with other available methods applicable for the described data and revealed its higher performance. We showed that our method has high specificity and sensitivity for high-resolution copy number detection in targeted sequencing data using large cohort of samples.

A new SPECT/CT reconstruction algorithm: reliability and accuracy in clinical routine for non-oncologic bone diseases.

PubMed

Delcroix, Olivier; Robin, Philippe; Gouillou, Maelenn; Le Duc-Pennec, Alexandra; Alavi, Zarrin; Le Roux, Pierre-Yves; Abgral, Ronan; Salaun, Pierre-Yves; Bourhis, David; Querellou, Solène

2018-02-12

xSPECT Bone® (xB) is a new reconstruction algorithm developed by Siemens® in bone hybrid imaging (SPECT/CT). A CT-based tissue segmentation is incorporated into SPECT reconstruction to provide SPECT images with bone anatomy appearance. The objectives of this study were to assess xB/CT reconstruction diagnostic reliability and accuracy in comparison with Flash 3D® (F3D)/CT in clinical routine. Two hundred thirteen consecutive patients referred to the Brest Nuclear Medicine Department for non-oncological bone diseases were evaluated retrospectively. Two hundred seven SPECT/CT were included. All SPECT/CT were independently interpreted by two nuclear medicine physicians (a junior and a senior expert) with xB/CT then with F3D/CT three months later. Inter-observer agreement (IOA) and diagnostic confidence were determined using McNemar test, and unweighted Kappa coefficient. The study objectives were then re-assessed for validation through > 18 months of clinical and paraclinical follow-up. No statistically significant differences between IOA xB and IOA F3D were found (p = 0.532). Agreement for xB after categorical classification of the diagnoses was high (κ xB = 0.89 [95% CI 0.84 -0.93]) but without statistically significant difference F3D (κ F3D = 0.90 [95% CI 0.86 - 0.94]). Thirty-one (14.9%) inter-reconstruction diagnostic discrepancies were observed of which 21 (10.1%) were classified as major. The follow-up confirmed the diagnosis of F3D in 10 cases, xB in 6 cases and was non-contributory in 5 cases. xB reconstruction algorithm was found reliable, providing high interobserver agreement and similar diagnostic confidence to F3D reconstruction in clinical routine.

Algorithm for Video Summarization of Bronchoscopy Procedures

PubMed Central

2011-01-01

Background The duration of bronchoscopy examinations varies considerably depending on the diagnostic and therapeutic procedures used. It can last more than 20 minutes if a complex diagnostic work-up is included. With wide access to videobronchoscopy, the whole procedure can be recorded as a video sequence. Common practice relies on an active attitude of the bronchoscopist who initiates the recording process and usually chooses to archive only selected views and sequences. However, it may be important to record the full bronchoscopy procedure as documentation when liability issues are at stake. Furthermore, an automatic recording of the whole procedure enables the bronchoscopist to focus solely on the performed procedures. Video recordings registered during bronchoscopies include a considerable number of frames of poor quality due to blurry or unfocused images. It seems that such frames are unavoidable due to the relatively tight endobronchial space, rapid movements of the respiratory tract due to breathing or coughing, and secretions which occur commonly in the bronchi, especially in patients suffering from pulmonary disorders. Methods The use of recorded bronchoscopy video sequences for diagnostic, reference and educational purposes could be considerably extended with efficient, flexible summarization algorithms. Thus, the authors developed a prototype system to create shortcuts (called summaries or abstracts) of bronchoscopy video recordings. Such a system, based on models described in previously published papers, employs image analysis methods to exclude frames or sequences of limited diagnostic or education value. Results The algorithm for the selection or exclusion of specific frames or shots from video sequences recorded during bronchoscopy procedures is based on several criteria, including automatic detection of "non-informative", frames showing the branching of the airways and frames including pathological lesions. Conclusions The paper focuses on the challenge of generating summaries of bronchoscopy video recordings. PMID:22185344

An audit of Cryptosporidium and Giardia detection in Scottish National Health Service Diagnostic Microbiology Laboratories.

PubMed

Alexander, C L; Currie, S; Pollock, K; Smith-Palmer, A; Jones, B L

2017-06-01

Giardia duodenalis and Cryptosporidium species are protozoan parasites capable of causing gastrointestinal disease in humans and animals through the ingestion of infective faeces. Whereas Cryptosporidium species can be acquired locally or through foreign travel, there is the mis-conception that giardiasis is considered to be largely travel-associated, which results in differences in laboratory testing algorithms. In order to determine the level of variation in testing criteria and detection methods between diagnostic laboratories for both pathogens across Scotland, an audit was performed. Twenty Scottish diagnostic microbiology laboratories were invited to participate with questions on sample acceptance criteria, testing methods, testing rates and future plans for pathogen detection. Reponses were received from 19 of the 20 laboratories representing each of the 14 territorial Health Boards. Detection methods varied between laboratories with the majority performing microscopy, one using a lateral flow immunochromatographic antigen assay, another using a manually washed plate-based enzyme immunoassay (EIA) and one laboratory trialling a plate-based EIA automated with an EIA plate washer. Whereas all laboratories except one screened every stool for Cryptosporidium species, an important finding was that significant variation in the testing algorithm for detecting Giardia was noted with only four laboratories testing all diagnostic stools. The most common criteria were 'travel history' (11 laboratories) and/or 'when requested' (14 laboratories). Despite only a small proportion of stools being examined in 15 laboratories for Giardia (2%-18% of the total number of stools submitted), of interest is the finding that a higher positivity rate was observed for Giardia than Cryptosporidium in 10 of these 15 laboratories. These findings highlight that the underreporting of Giardia in Scotland is likely based on current selection and testing algorithms.

[Intelligent systems tools in the diagnosis of acute coronary syndromes: A systemic review].

PubMed

Sprockel, John; Tejeda, Miguel; Yate, José; Diaztagle, Juan; González, Enrique

2017-03-27

Acute myocardial infarction is the leading cause of non-communicable deaths worldwide. Its diagnosis is a highly complex task, for which modelling through automated methods has been attempted. A systematic review of the literature was performed on diagnostic tests that applied intelligent systems tools in the diagnosis of acute coronary syndromes. A systematic review of the literature is presented using Medline, Embase, Scopus, IEEE/IET Electronic Library, ISI Web of Science, Latindex and LILACS databases for articles that include the diagnostic evaluation of acute coronary syndromes using intelligent systems. The review process was conducted independently by 2 reviewers, and discrepancies were resolved through the participation of a third person. The operational characteristics of the studied tools were extracted. A total of 35 references met the inclusion criteria. In 22 (62.8%) cases, neural networks were used. In five studies, the performances of several intelligent systems tools were compared. Thirteen studies sought to perform diagnoses of all acute coronary syndromes, and in 22, only infarctions were studied. In 21 cases, clinical and electrocardiographic aspects were used as input data, and in 10, only electrocardiographic data were used. Most intelligent systems use the clinical context as a reference standard. High rates of diagnostic accuracy were found with better performance using neural networks and support vector machines, compared with statistical tools of pattern recognition and decision trees. Extensive evidence was found that shows that using intelligent systems tools achieves a greater degree of accuracy than some clinical algorithms or scales and, thus, should be considered appropriate tools for supporting diagnostic decisions of acute coronary syndromes. Copyright © 2017 Instituto Nacional de Cardiología Ignacio Chávez. Publicado por Masson Doyma México S.A. All rights reserved.

Influence of reconstruction algorithms on image quality in SPECT myocardial perfusion imaging.

PubMed

Davidsson, Anette; Olsson, Eva; Engvall, Jan; Gustafsson, Agnetha

2017-11-01

We investigated if image- and diagnostic quality in SPECT MPI could be maintained despite a reduced acquisition time adding Depth Dependent Resolution Recovery (DDRR) for image reconstruction. Images were compared with filtered back projection (FBP) and iterative reconstruction using Ordered Subsets Expectation Maximization with (IRAC) and without (IRNC) attenuation correction (AC). Stress- and rest imaging for 15 min was performed on 21 subjects with a dual head gamma camera (Infinia Hawkeye; GE Healthcare), ECG-gating with 8 frames/cardiac cycle and a low-dose CT-scan. A 9 min acquisition was generated using five instead of eight gated frames and was reconstructed with DDRR, with (IRACRR) and without AC (IRNCRR) as well as with FBP. Three experienced nuclear medicine specialists visually assessed anonymized images according to eight criteria on a four point scale, three related to image quality and five to diagnostic confidence. Statistical analysis was performed using Visual Grading Regression (VGR). Observer confidence in statements on image quality was highest for the images that were reconstructed using DDRR (P<0·01 compared to FBP). Iterative reconstruction without DDRR was not superior to FBP. Interobserver variability was significant for statements on image quality (P<0·05) but lower in the diagnostic statements on ischemia and scar. The confidence in assessing ischemia and scar was not different between the reconstruction techniques (P = n.s.). SPECT MPI collected in 9 min, reconstructed with DDRR and AC, produced better image quality than the standard procedure. The observers expressed the highest diagnostic confidence in the DDRR reconstruction. © 2016 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

The influence of parental concern on the utility of autism diagnostic instruments.

PubMed

Havdahl, Karoline Alexandra; Bishop, Somer L; Surén, Pål; Øyen, Anne-Siri; Lord, Catherine; Pickles, Andrew; von Tetzchner, Stephen; Schjølberg, Synnve; Gunnes, Nina; Hornig, Mady; Lipkin, W Ian; Susser, Ezra; Bresnahan, Michaeline; Magnus, Per; Stenberg, Nina; Reichborn-Kjennerud, Ted; Stoltenberg, Camilla

2017-10-01

The parental report-based Autism Diagnostic Interview-Revised (ADI-R) and the clinician observation-based Autism Diagnostic Observation Schedule (ADOS) have been validated primarily in U.S. clinics specialized in autism spectrum disorder (ASD), in which most children are referred by their parents because of ASD concern. This study assessed diagnostic agreement of the ADOS-2 and ADI-R toddler algorithms in a more broadly based sample of 679 toddlers (age 35-47 months) from the Norwegian Mother and Child Cohort. We also examined whether parental concern about ASD influenced instrument performance, comparing toddlers identified based on parental ASD concern (n = 48) and parent-reported signs of developmental problems (screening) without a specific concern about ASD (n = 400). The ADOS cutoffs showed consistently well-balanced sensitivity and specificity. The ADI-R cutoffs demonstrated good specificity, but reduced sensitivity, missing 43% of toddlers whose parents were not specifically concerned about ASD. The ADI-R and ADOS dimensional scores agreed well with clinical diagnoses (area under the curve ≥ 0.85), contributing additively to their prediction. On the ADI-R, different cutoffs were needed according to presence or absence of parental ASD concern, in order to achieve comparable balance of sensitivity and specificity. These results highlight the importance of taking parental concern about ASD into account when interpreting scores from parental report-based instruments such as the ADI-R. While the ADOS cutoffs performed consistently well, the additive contributions of ADI-R and ADOS scores to the prediction of ASD diagnosis underscore the value of combining instruments based on parent accounts and clinician observation in evaluation of ASD. Autism Res 2017, 10: 1672-1686. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Quantitative Evaluation of Automated Skull-Stripping Methods Applied to Contemporary and Legacy Images: Effects of Diagnosis, Bias Correction, and Slice Location

PubMed Central

Fennema-Notestine, Christine; Ozyurt, I. Burak; Clark, Camellia P.; Morris, Shaunna; Bischoff-Grethe, Amanda; Bondi, Mark W.; Jernigan, Terry L.; Fischl, Bruce; Segonne, Florent; Shattuck, David W.; Leahy, Richard M.; Rex, David E.; Toga, Arthur W.; Zou, Kelly H.; BIRN, Morphometry; Brown, Gregory G.

2008-01-01

Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four methods: Brain Extraction Tool (BET; Smith [2002]: Hum Brain Mapp 17:143–155); 3dIntracranial (Ward [1999] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage 22:1060–1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41–54; Shattuck et al. [2001] Neuroimage 13:856 – 876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T1-weighted image sets; and four diagnostic groups (depressed, Alzheimer’s, young and elderly control). To provide a criterion for outcome assessment, two experts manually stripped six sagittal sections for each dataset in locations where brain and nonbrain tissue are difficult to distinguish. Methods were compared on Jaccard similarity coefficients, Hausdorff distances, and an Expectation-Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more robust across diagnostic groups compared with 3dIntracranial and BET. With respect to specificity, BSE tended to perform best across all groups, whereas HWA was more sensitive than other methods. The results of this study may direct users towards a method appropriate to their T1-weighted datasets and improve the efficiency of processing for large, multisite neuroimaging studies. PMID:15986433

Prospective Evaluation of Prior Image Constrained Compressed Sensing (PICCS) Algorithm in Abdominal CT: A comparison of reduced dose with standard dose imaging

PubMed Central

Lubner, Meghan G.; Pickhardt, Perry J.; Kim, David H.; Tang, Jie; Munoz del Rio, Alejandro; Chen, Guang-Hong

2014-01-01

Purpose To prospectively study CT dose reduction using the “prior image constrained compressed sensing” (PICCS) reconstruction technique. Methods Immediately following routine standard dose (SD) abdominal MDCT, 50 patients (mean age, 57.7 years; mean BMI, 28.8) underwent a second reduced-dose (RD) scan (targeted dose reduction, 70-90%). DLP, CTDIvol and SSDE were compared. Several reconstruction algorithms (FBP, ASIR, and PICCS) were applied to the RD series. SD images with FBP served as reference standard. Two blinded readers evaluated each series for subjective image quality and focal lesion detection. Results Mean DLP, CTDIvol, and SSDE for RD series was 140.3 mGy*cm (median 79.4), 3.7 mGy (median 1.8), and 4.2 mGy (median 2.3) compared with 493.7 mGy*cm (median 345.8), 12.9 mGy (median 7.9 mGy) and 14.6 mGy (median 10.1) for SD series, respectively. Mean effective patient diameter was 30.1 cm (median 30), which translates to a mean SSDE reduction of 72% (p<0.001). RD-PICCS image quality score was 2.8±0.5, improved over the RD-FBP (1.7±0.7) and RD-ASIR(1.9±0.8)(p<0.001), but lower than SD (3.5±0.5)(p<0.001). Readers detected 81% (184/228) of focal lesions on RD-PICCS series, versus 67% (153/228) and 65% (149/228) for RD-FBP and RD-ASIR, respectively. Mean image noise was significantly reduced on RD-PICCS series (13.9 HU) compared with RD-FBP (57.2) and RD-ASIR (44.1) (p<0.001). Conclusion PICCS allows for marked dose reduction at abdominal CT with improved image quality and diagnostic performance over reduced-dose FBP and ASIR. Further study is needed to determine indication-specific dose reduction levels that preserve acceptable diagnostic accuracy relative to higher-dose protocols. PMID:24943136

Implementation of an Integrated On-Board Aircraft Engine Diagnostic Architecture

NASA Technical Reports Server (NTRS)

Armstrong, Jeffrey B.; Simon, Donald L.

2012-01-01

An on-board diagnostic architecture for aircraft turbofan engine performance trending, parameter estimation, and gas-path fault detection and isolation has been developed and evaluated in a simulation environment. The architecture incorporates two independent models: a realtime self-tuning performance model providing parameter estimates and a performance baseline model for diagnostic purposes reflecting long-term engine degradation trends. This architecture was evaluated using flight profiles generated from a nonlinear model with realistic fleet engine health degradation distributions and sensor noise. The architecture was found to produce acceptable estimates of engine health and unmeasured parameters, and the integrated diagnostic algorithms were able to perform correct fault isolation in approximately 70 percent of the tested cases

[Role of cytology in hematopathological diagnostics].

PubMed

Bode, B; Tinguely, M

2012-07-01

The role of cytology has so far been underrecognized in the diagnostic process of hematopathological questions. This article presents an algorithm which allows a stepwise work-up of cytology specimens obtained by minimally invasive ultrasound-guided fine needle aspiration in patients with unexplained lymph node swelling. Moreover, it is shown how the selective separation of cytology specimens allows the application of immunophenotypic analysis including flow cytometry and immunohistochemistry as well as molecular analyses, such as fluorescence in situ hybridization (FISH) and polymerase chain reaction (PCR) strategies. With the integrative procedure presented, cytology offers an excellent cost-effective tool for the diagnostic approach of patients with suspected hematopathological malignancies allowing a high diagnostic accuracy, ideal for initial diagnosis or follow-up.

Towards an unsupervised device for the diagnosis of childhood pneumonia in low resource settings: automatic segmentation of respiratory sounds.

PubMed

Sola, J; Braun, F; Muntane, E; Verjus, C; Bertschi, M; Hugon, F; Manzano, S; Benissa, M; Gervaix, A

2016-08-01

Pneumonia remains the worldwide leading cause of children mortality under the age of five, with every year 1.4 million deaths. Unfortunately, in low resource settings, very limited diagnostic support aids are provided to point-of-care practitioners. Current UNICEF/WHO case management algorithm relies on the use of a chronometer to manually count breath rates on pediatric patients: there is thus a major need for more sophisticated tools to diagnose pneumonia that increase sensitivity and specificity of breath-rate-based algorithms. These tools should be low cost, and adapted to practitioners with limited training. In this work, a novel concept of unsupervised tool for the diagnosis of childhood pneumonia is presented. The concept relies on the automated analysis of respiratory sounds as recorded by a point-of-care electronic stethoscope. By identifying the presence of auscultation sounds at different chest locations, this diagnostic tool is intended to estimate a pneumonia likelihood score. After presenting the overall architecture of an algorithm to estimate pneumonia scores, the importance of a robust unsupervised method to identify inspiratory and expiratory phases of a respiratory cycle is highlighted. Based on data from an on-going study involving pediatric pneumonia patients, a first algorithm to segment respiratory sounds is suggested. The unsupervised algorithm relies on a Mel-frequency filter bank, a two-step Gaussian Mixture Model (GMM) description of data, and a final Hidden Markov Model (HMM) interpretation of inspiratory-expiratory sequences. Finally, illustrative results on first recruited patients are provided. The presented algorithm opens the doors to a new family of unsupervised respiratory sound analyzers that could improve future versions of case management algorithms for the diagnosis of pneumonia in low-resources settings.

A novel vibration-based fault diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement

NASA Astrophysics Data System (ADS)

Hong, Liu; Qu, Yongzhi; Dhupia, Jaspreet Singh; Sheng, Shuangwen; Tan, Yuegang; Zhou, Zude

2017-09-01

The localized failures of gears introduce cyclic-transient impulses in the measured gearbox vibration signals. These impulses are usually identified from the sidebands around gear-mesh harmonics through the spectral analysis of cyclo-stationary signals. However, in practice, several high-powered applications of gearboxes like wind turbines are intrinsically characterized by nonstationary processes that blur the measured vibration spectra of a gearbox and deteriorate the efficacy of spectral diagnostic methods. Although order-tracking techniques have been proposed to improve the performance of spectral diagnosis for nonstationary signals measured in such applications, the required hardware for the measurement of rotational speed of these machines is often unavailable in industrial settings. Moreover, existing tacho-less order-tracking approaches are usually limited by the high time-frequency resolution requirement, which is a prerequisite for the precise estimation of the instantaneous frequency. To address such issues, a novel fault-signature enhancement algorithm is proposed that can alleviate the spectral smearing without the need of rotational speed measurement. This proposed tacho-less diagnostic technique resamples the measured acceleration signal of the gearbox based on the optimal warping path evaluated from the fast dynamic time-warping algorithm, which aligns a filtered shaft rotational harmonic signal with respect to a reference signal assuming a constant shaft rotational speed estimated from the approximation of operational speed. The effectiveness of this method is validated using both simulated signals from a fixed-axis gear pair under nonstationary conditions and experimental measurements from a 750-kW planetary wind turbine gearbox on a dynamometer test rig. The results demonstrate that the proposed algorithm can identify fault information from typical gearbox vibration measurements carried out in a resource-constrained industrial environment.

A novel vibration-based fault diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hong, Liu; Qu, Yongzhi; Dhupia, Jaspreet Singh

The localized failures of gears introduce cyclic-transient impulses in the measured gearbox vibration signals. These impulses are usually identified from the sidebands around gear-mesh harmonics through the spectral analysis of cyclo-stationary signals. However, in practice, several high-powered applications of gearboxes like wind turbines are intrinsically characterized by nonstationary processes that blur the measured vibration spectra of a gearbox and deteriorate the efficacy of spectral diagnostic methods. Although order-tracking techniques have been proposed to improve the performance of spectral diagnosis for nonstationary signals measured in such applications, the required hardware for the measurement of rotational speed of these machines is oftenmore » unavailable in industrial settings. Moreover, existing tacho-less order-tracking approaches are usually limited by the high time-frequency resolution requirement, which is a prerequisite for the precise estimation of the instantaneous frequency. To address such issues, a novel fault-signature enhancement algorithm is proposed that can alleviate the spectral smearing without the need of rotational speed measurement. This proposed tacho-less diagnostic technique resamples the measured acceleration signal of the gearbox based on the optimal warping path evaluated from the fast dynamic time-warping algorithm, which aligns a filtered shaft rotational harmonic signal with respect to a reference signal assuming a constant shaft rotational speed estimated from the approximation of operational speed. The effectiveness of this method is validated using both simulated signals from a fixed-axis gear pair under nonstationary conditions and experimental measurements from a 750-kW planetary wind turbine gearbox on a dynamometer test rig. Lastly, the results demonstrate that the proposed algorithm can identify fault information from typical gearbox vibration measurements carried out in a resource-constrained industrial environment.« less

A novel vibration-based fault diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement

DOE PAGES

Hong, Liu; Qu, Yongzhi; Dhupia, Jaspreet Singh; ...

2017-02-27

The localized failures of gears introduce cyclic-transient impulses in the measured gearbox vibration signals. These impulses are usually identified from the sidebands around gear-mesh harmonics through the spectral analysis of cyclo-stationary signals. However, in practice, several high-powered applications of gearboxes like wind turbines are intrinsically characterized by nonstationary processes that blur the measured vibration spectra of a gearbox and deteriorate the efficacy of spectral diagnostic methods. Although order-tracking techniques have been proposed to improve the performance of spectral diagnosis for nonstationary signals measured in such applications, the required hardware for the measurement of rotational speed of these machines is oftenmore » unavailable in industrial settings. Moreover, existing tacho-less order-tracking approaches are usually limited by the high time-frequency resolution requirement, which is a prerequisite for the precise estimation of the instantaneous frequency. To address such issues, a novel fault-signature enhancement algorithm is proposed that can alleviate the spectral smearing without the need of rotational speed measurement. This proposed tacho-less diagnostic technique resamples the measured acceleration signal of the gearbox based on the optimal warping path evaluated from the fast dynamic time-warping algorithm, which aligns a filtered shaft rotational harmonic signal with respect to a reference signal assuming a constant shaft rotational speed estimated from the approximation of operational speed. The effectiveness of this method is validated using both simulated signals from a fixed-axis gear pair under nonstationary conditions and experimental measurements from a 750-kW planetary wind turbine gearbox on a dynamometer test rig. Lastly, the results demonstrate that the proposed algorithm can identify fault information from typical gearbox vibration measurements carried out in a resource-constrained industrial environment.« less

Design of the algorithm of photons migration in the multilayer skin structure

NASA Astrophysics Data System (ADS)

Bulykina, Anastasiia B.; Ryzhova, Victoria A.; Korotaev, Valery V.; Samokhin, Nikita Y.

2017-06-01

Design of approaches and methods of the oncological diseases diagnostics has special significance. It allows determining any kind of tumors at early stages. The development of optical and laser technologies provided increase of a number of methods allowing making diagnostic studies of oncological diseases. A promising area of biomedical diagnostics is the development of automated nondestructive testing systems for the study of the skin polarizing properties based on backscattered radiation detection. Specification of the examined tissue polarizing properties allows studying of structural properties change influenced by various pathologies. Consequently, measurement and analysis of the polarizing properties of the scattered optical radiation for the development of methods for diagnosis and imaging of skin in vivo appear relevant. The purpose of this research is to design the algorithm of photons migration in the multilayer skin structure. In this research, the algorithm of photons migration in the multilayer skin structure was designed. It is based on the use of the Monte Carlo method. Implemented Monte Carlo method appears as a tracking the paths of photons experiencing random discrete direction changes before they are released from the analyzed area or decrease their intensity to negligible levels. Modeling algorithm consists of the medium and the source characteristics generation, a photon generating considering spatial coordinates of the polar and azimuthal angles, the photon weight reduction calculating due to specular and diffuse reflection, the photon mean free path definition, the photon motion direction angle definition as a result of random scattering with a Henyey-Greenstein phase function, the medium's absorption calculation. Biological tissue is modeled as a homogeneous scattering sheet characterized by absorption, a scattering and anisotropy coefficients.

Diagnostic Approach to a Patient With Paraneoplastic Neurological Syndrome.

PubMed

Mahta, Ali; Vijayvergia, Namrata; Bhavsar, Tapan M; Ward, Lawrence D

2012-10-01

Herein, we discussed a case of an otherwise healthy man who presented with progressive gait imbalance and ataxia, found to have small cell lung cancer. Based upon our clinical findings and laboratory data, a diagnosis of paraneoplastic cerebellar degeneration was made. Paraneoplastic neurological syndromes (PNS) are relatively rare but diverse and always should be considered in differentials. A diagnostic algorithm along with appropriate work up is discussed here.

Diagnostic Approach to a Patient With Paraneoplastic Neurological Syndrome

PubMed Central

Mahta, Ali; Vijayvergia, Namrata; Bhavsar, Tapan M.; Ward, Lawrence D.

2012-01-01

Herein, we discussed a case of an otherwise healthy man who presented with progressive gait imbalance and ataxia, found to have small cell lung cancer. Based upon our clinical findings and laboratory data, a diagnosis of paraneoplastic cerebellar degeneration was made. Paraneoplastic neurological syndromes (PNS) are relatively rare but diverse and always should be considered in differentials. A diagnostic algorithm along with appropriate work up is discussed here. PMID:29147315

Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

2006-03-01

Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

A parallelizable real-time motion tracking algorithm with applications to ultrasonic strain imaging

NASA Astrophysics Data System (ADS)

Jiang, J.; Hall, T. J.

2007-07-01

Ultrasound-based mechanical strain imaging systems utilize signals from conventional diagnostic ultrasound systems to image tissue elasticity contrast that provides new diagnostically valuable information. Previous works (Hall et al 2003 Ultrasound Med. Biol. 29 427, Zhu and Hall 2002 Ultrason. Imaging 24 161) demonstrated that uniaxial deformation with minimal elevation motion is preferred for breast strain imaging and real-time strain image feedback to operators is important to accomplish this goal. The work reported here enhances the real-time speckle tracking algorithm with two significant modifications. One fundamental change is that the proposed algorithm is a column-based algorithm (a column is defined by a line of data parallel to the ultrasound beam direction, i.e. an A-line), as opposed to a row-based algorithm (a row is defined by a line of data perpendicular to the ultrasound beam direction). Then, displacement estimates from its adjacent columns provide good guidance for motion tracking in a significantly reduced search region to reduce computational cost. Consequently, the process of displacement estimation can be naturally split into at least two separated tasks, computed in parallel, propagating outward from the center of the region of interest (ROI). The proposed algorithm has been implemented and optimized in a Windows® system as a stand-alone ANSI C++ program. Results of preliminary tests, using numerical and tissue-mimicking phantoms, and in vivo tissue data, suggest that high contrast strain images can be consistently obtained with frame rates (10 frames s-1) that exceed our previous methods.

Diagnostic Concordance between DSM-5 and ICD-10 Cannabis Use Disorders.

PubMed

Proctor, Steven L; Williams, Daniel C; Kopak, Albert M; Voluse, Andrew C; Connolly, Kevin M; Hoffmann, Norman G

2016-07-01

With the recent federal mandate that all U.S. health care settings transition to ICD-10 billing codes, empirical evidence is necessary to determine if the DSM-5 designations map to their respective ICD-10 diagnostic categories/billing codes. The present study examined the concordance between DSM-5 and ICD-10 cannabis use disorder diagnoses. Data were derived from routine clinical assessments of 6871 male and 801 female inmates recently admitted to a state prison system from 2000 to 2003. DSM-5 and ICD-10 diagnostic determinations were made from algorithms corresponding to the respective diagnostic formulations. Past 12-month prevalence rates of cannabis use disorders were comparable across classification systems. The vast majority of inmates with no DSM-5 diagnosis continued to have no diagnosis per the ICD-10, and a similar proportion with a DSM-5 severe diagnosis received an ICD-10 dependence diagnosis. Most of the variation in diagnostic classifications was accounted for by those with a DSM-5 moderate diagnosis in that approximately half of these cases received an ICD-10 dependence diagnosis while the remaining cases received a harmful use diagnosis. Although there appears to be a generally high level of agreement between diagnostic classification systems for those with no diagnosis or those evincing symptoms of a more severe condition, concordance between DSM-5 moderate and ICD-10 dependence diagnoses was poor. Additional research is warranted to determine the appropriateness and implications of the current DSM-5 coding guidelines regarding the assignment of an ICD-10 dependence code for those with a DSM-5 moderate diagnosis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Potential of non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Shaohua; Wang, Lan; Chen, Weisheng; Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Li, Buhong; Chen, Rong

2014-11-01

Non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy (SERS) analysis was presented. Urine SERS spectra were measured on esophagus cancer patients (n = 56) and healthy volunteers (n = 36) for control analysis. Tentative assignments of the urine SERS spectra indicated some interesting esophagus cancer-specific biomolecular changes, including a decrease in the relative content of urea and an increase in the percentage of uric acid in the urine of esophagus cancer patients compared to that of healthy subjects. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze and differentiate the SERS spectra between normal and esophagus cancer urine. The diagnostic algorithms utilizing a multivariate analysis method achieved a diagnostic sensitivity of 89.3% and specificity of 83.3% for separating esophagus cancer samples from normal urine samples. These results from the explorative work suggested that silver nano particle-based urine SERS analysis coupled with PCA-LDA multivariate analysis has potential for non-invasive detection of esophagus cancer.

A real-time algorithm for the harmonic estimation and frequency tracking of dominant components in fusion plasma magnetic diagnostics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alves, D.; Coelho, R.; Collaboration: JET-EFDA Contributors

2013-08-15

The real-time tracking of instantaneous quantities such as frequency, amplitude, and phase of components immerse in noisy signals has been a common problem in many scientific and engineering fields such as power systems and delivery, telecommunications, and acoustics for the past decades. In magnetically confined fusion research, extracting this sort of information from magnetic signals can be of valuable assistance in, for instance, feedback control of detrimental magnetohydrodynamic modes and disruption avoidance mechanisms by monitoring instability growth or anticipating mode-locking events. This work is focused on nonlinear Kalman filter based methods for tackling this problem. Similar methods have already provenmore » their merits and have been successfully employed in this scientific domain in applications such as amplitude demodulation for the motional Stark effect diagnostic. In the course of this work, three approaches are described, compared, and discussed using magnetic signals from the Joint European Torus tokamak plasma discharges for benchmarking purposes.« less