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Sample records for model based diagnosis

  1. Efficient Model-Based Diagnosis Engine

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Vatan, Farrokh; Barrett, Anthony; James, Mark; Mackey, Ryan; Williams, Colin

    2009-01-01

    An efficient diagnosis engine - a combination of mathematical models and algorithms - has been developed for identifying faulty components in a possibly complex engineering system. This model-based diagnosis engine embodies a twofold approach to reducing, relative to prior model-based diagnosis engines, the amount of computation needed to perform a thorough, accurate diagnosis. The first part of the approach involves a reconstruction of the general diagnostic engine to reduce the complexity of the mathematical-model calculations and of the software needed to perform them. The second part of the approach involves algorithms for computing a minimal diagnosis (the term "minimal diagnosis" is defined below). A somewhat lengthy background discussion is prerequisite to a meaningful summary of the innovative aspects of the present efficient model-based diagnosis engine. In model-based diagnosis, the function of each component and the relationships among all the components of the engineering system to be diagnosed are represented as a logical system denoted the system description (SD). Hence, the expected normal behavior of the engineering system is the set of logical consequences of the SD. Faulty components lead to inconsistencies between the observed behaviors of the system and the SD (see figure). Diagnosis - the task of finding faulty components - is reduced to finding those components, the abnormalities of which could explain all the inconsistencies. The solution of the diagnosis problem should be a minimal diagnosis, which is a minimal set of faulty components. A minimal diagnosis stands in contradistinction to the trivial solution, in which all components are deemed to be faulty, and which, therefore, always explains all inconsistencies.

  2. Model-based reconfiguration: Diagnosis and recovery

    NASA Technical Reports Server (NTRS)

    Crow, Judy; Rushby, John

    1994-01-01

    We extend Reiter's general theory of model-based diagnosis to a theory of fault detection, identification, and reconfiguration (FDIR). The generality of Reiter's theory readily supports an extension in which the problem of reconfiguration is viewed as a close analog of the problem of diagnosis. Using a reconfiguration predicate 'rcfg' analogous to the abnormality predicate 'ab,' we derive a strategy for reconfiguration by transforming the corresponding strategy for diagnosis. There are two obvious benefits of this approach: algorithms for diagnosis can be exploited as algorithms for reconfiguration and we have a theoretical framework for an integrated approach to FDIR. As a first step toward realizing these benefits we show that a class of diagnosis engines can be used for reconfiguration and we discuss algorithms for integrated FDIR. We argue that integrating recovery and diagnosis is an essential next step if this technology is to be useful for practical applications.

  3. Fast Algorithms for Model-Based Diagnosis

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Barrett, Anthony; Vatan, Farrokh; Mackey, Ryan

    2005-01-01

    Two improved new methods for automated diagnosis of complex engineering systems involve the use of novel algorithms that are more efficient than prior algorithms used for the same purpose. Both the recently developed algorithms and the prior algorithms in question are instances of model-based diagnosis, which is based on exploring the logical inconsistency between an observation and a description of a system to be diagnosed. As engineering systems grow more complex and increasingly autonomous in their functions, the need for automated diagnosis increases concomitantly. In model-based diagnosis, the function of each component and the interconnections among all the components of the system to be diagnosed (for example, see figure) are represented as a logical system, called the system description (SD). Hence, the expected behavior of the system is the set of logical consequences of the SD. Faulty components lead to inconsistency between the observed behaviors of the system and the SD. The task of finding the faulty components (diagnosis) reduces to finding the components, the abnormalities of which could explain all the inconsistencies. Of course, the meaningful solution should be a minimal set of faulty components (called a minimal diagnosis), because the trivial solution, in which all components are assumed to be faulty, always explains all inconsistencies. Although the prior algorithms in question implement powerful methods of diagnosis, they are not practical because they essentially require exhaustive searches among all possible combinations of faulty components and therefore entail the amounts of computation that grow exponentially with the number of components of the system.

  4. Distributed real-time model-based diagnosis

    NASA Technical Reports Server (NTRS)

    Barrett, A. C.; Chung, S. H.

    2003-01-01

    This paper presents an approach to onboard anomaly diagnosis that combines the simplicity and real-time guarantee of a rule-based diagnosis system with the specification ease and coverage guarantees of a model-based diagnosis system.

  5. Fault diagnosis based on continuous simulation models

    NASA Technical Reports Server (NTRS)

    Feyock, Stefan

    1987-01-01

    The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.

  6. Qualitative model-based diagnosis using possibility theory

    NASA Technical Reports Server (NTRS)

    Joslyn, Cliff

    1994-01-01

    The potential for the use of possibility in the qualitative model-based diagnosis of spacecraft systems is described. The first sections of the paper briefly introduce the Model-Based Diagnostic (MBD) approach to spacecraft fault diagnosis; Qualitative Modeling (QM) methodologies; and the concepts of possibilistic modeling in the context of Generalized Information Theory (GIT). Then the necessary conditions for the applicability of possibilistic methods to qualitative MBD, and a number of potential directions for such an application, are described.

  7. The use of multiple models in case-based diagnosis

    NASA Technical Reports Server (NTRS)

    Karamouzis, Stamos T.; Feyock, Stefan

    1993-01-01

    The work described in this paper has as its goal the integration of a number of reasoning techniques into a unified intelligent information system that will aid flight crews with malfunction diagnosis and prognostication. One of these approaches involves using the extensive archive of information contained in aircraft accident reports along with various models of the aircraft as the basis for case-based reasoning about malfunctions. Case-based reasoning draws conclusions on the basis of similarities between the present situation and prior experience. We maintain that the ability of a CBR program to reason about physical systems is significantly enhanced by the addition to the CBR program of various models. This paper describes the diagnostic concepts implemented in a prototypical case based reasoner that operates in the domain of in-flight fault diagnosis, the various models used in conjunction with the reasoner's CBR component, and results from a preliminary evaluation.

  8. Biased Randomized Algorithm for Fast Model-Based Diagnosis

    NASA Technical Reports Server (NTRS)

    Williams, Colin; Vartan, Farrokh

    2005-01-01

    A biased randomized algorithm has been developed to enable the rapid computational solution of a propositional- satisfiability (SAT) problem equivalent to a diagnosis problem. The closest competing methods of automated diagnosis are described in the preceding article "Fast Algorithms for Model-Based Diagnosis" and "Two Methods of Efficient Solution of the Hitting-Set Problem" (NPO-30584), which appears elsewhere in this issue. It is necessary to recapitulate some of the information from the cited articles as a prerequisite to a description of the present method. As used here, "diagnosis" signifies, more precisely, a type of model-based diagnosis in which one explores any logical inconsistencies between the observed and expected behaviors of an engineering system. The function of each component and the interconnections among all the components of the engineering system are represented as a logical system. Hence, the expected behavior of the engineering system is represented as a set of logical consequences. Faulty components lead to inconsistency between the observed and expected behaviors of the system, represented by logical inconsistencies. Diagnosis - the task of finding the faulty components - reduces to finding the components, the abnormalities of which could explain all the logical inconsistencies. One seeks a minimal set of faulty components (denoted a minimal diagnosis), because the trivial solution, in which all components are deemed to be faulty, always explains all inconsistencies. In the methods of the cited articles, the minimal-diagnosis problem is treated as equivalent to a minimal-hitting-set problem, which is translated from a combinatorial to a computational problem by mapping it onto the Boolean-satisfiability and integer-programming problems. The integer-programming approach taken in one of the prior methods is complete (in the sense that it is guaranteed to find a solution if one exists) and slow and yields a lower bound on the size of the

  9. Diagnosing Students' Mental Models via the Web-Based Mental Models Diagnosis System

    ERIC Educational Resources Information Center

    Wang, Tzu-Hua; Chiu, Mei-Hung; Lin, Jing-Wen; Chou, Chin-Cheng

    2013-01-01

    Mental models play an important role in science education research. To extend the effectiveness of conceptual change research and to improve mental model identi?cation and diagnosis, the authors developed and tested the Web-Based Mental Models Diagnosis (WMMD) system. In this article, they describe their WMMD system, which goes beyond the…

  10. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

    PubMed Central

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  11. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory.

    PubMed

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-18

    Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.

  12. An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Narasimhan, Sriram; Roychoudhury, Indranil; Daigle, Matthew; Pulido, Belarmino

    2013-01-01

    Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.

  13. Improving model-based diagnosis through algebraic analysis: The Petri net challenge

    SciTech Connect

    Portinale, L.

    1996-12-31

    The present paper describes the empirical evaluation of a linear algebra approach to model-based diagnosis, in case the behavioral model of the device under examination is described through a Petri net model. In particular, we show that algebraic analysis based on P-invariants of the net model, can significantly improve the performance of a model-based diagnostic system, while keeping the integrity of a general framework defined from a formal logical theory. A system called INVADS is described and experimental results, performed on a car fault domain and involving the comparison of different implementations of P-invariant based diagnosis, are then discussed.

  14. Model-Based Diagnosis in a Power Distribution Test-Bed

    NASA Technical Reports Server (NTRS)

    Scarl, E.; McCall, K.

    1998-01-01

    The Rodon model-based diagnosis shell was applied to a breadboard test-bed, modeling an automated power distribution system. The constraint-based modeling paradigm and diagnostic algorithm were found to adequately represent the selected set of test scenarios.

  15. An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Daigle, Matthew J.; Roychoudhury, Indranil

    2012-01-01

    Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

  16. Diagnosis by integrating model-based reasoning with knowledge-based reasoning

    NASA Technical Reports Server (NTRS)

    Bylander, Tom

    1988-01-01

    Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.

  17. Online model-based diagnosis to support autonomous operation of an advanced life support system

    NASA Technical Reports Server (NTRS)

    Biswas, Gautam; Manders, Eric-Jan; Ramirez, John; Mahadevan, Nagabhusan; Abdelwahed, Sherif

    2004-01-01

    This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.

  18. Online model-based diagnosis to support autonomous operation of an advanced life support system.

    PubMed

    Biswas, Gautam; Manders, Eric-Jan; Ramirez, John; Mahadevan, Nagabhusan; Abdelwahed, Sherif

    2004-01-01

    This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed. PMID:15880907

  19. Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft

    NASA Astrophysics Data System (ADS)

    Desbazeille, M.; Randall, R. B.; Guillet, F.; El Badaoui, M.; Hoisnard, C.

    2010-07-01

    This work aims at monitoring large diesel engines by analyzing the crankshaft angular speed variations. It focuses on a powerful 20-cylinder diesel engine with crankshaft natural frequencies within the operating speed range. First, the angular speed variations are modeled at the crankshaft free end. This includes modeling both the crankshaft dynamical behavior and the excitation torques. As the engine is very large, the first crankshaft torsional modes are in the low frequency range. A model with the assumption of a flexible crankshaft is required. The excitation torques depend on the in-cylinder pressure curve. The latter is modeled with a phenomenological model. Mechanical and combustion parameters of the model are optimized with the help of actual data. Then, an automated diagnosis based on an artificially intelligent system is proposed. Neural networks are used for pattern recognition of the angular speed waveforms in normal and faulty conditions. Reference patterns required in the training phase are computed with the model, calibrated using a small number of actual measurements. Promising results are obtained. An experimental fuel leakage fault is successfully diagnosed, including detection and localization of the faulty cylinder, as well as the approximation of the fault severity.

  20. Fault diagnosis using noise modeling and a new artificial immune system based algorithm

    NASA Astrophysics Data System (ADS)

    Abbasi, Farshid; Mojtahedi, Alireza; Ettefagh, Mir Mohammad

    2015-12-01

    A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

  1. The Analysis of Organizational Diagnosis on Based Six Box Model in Universities

    ERIC Educational Resources Information Center

    Hamid, Rahimi; Siadat, Sayyed Ali; Reza, Hoveida; Arash, Shahin; Ali, Nasrabadi Hasan; Azizollah, Arbabisarjou

    2011-01-01

    Purpose: The analysis of organizational diagnosis on based six box model at universities. Research method: Research method was descriptive-survey. Statistical population consisted of 1544 faculty members of universities which through random strafed sampling method 218 persons were chosen as the sample. Research Instrument were organizational…

  2. Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Lo, Chun; Lynch, Jerome P.; Liu, Mingyan

    2016-01-01

    Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.

  3. Livingstone Model-Based Diagnosis of Earth Observing One Infusion Experiment

    NASA Technical Reports Server (NTRS)

    Hayden, Sandra C.; Sweet, Adam J.; Christa, Scott E.

    2004-01-01

    The Earth Observing One satellite, launched in November 2000, is an active earth science observation platform. This paper reports on the progress of an infusion experiment in which the Livingstone 2 Model-Based Diagnostic engine is deployed on Earth Observing One, demonstrating the capability to monitor the nominal operation of the spacecraft under command of an on-board planner, and demonstrating on-board diagnosis of spacecraft failures. Design and development of the experiment, specification and validation of diagnostic scenarios, characterization of performance results and benefits of the model- based approach are presented.

  4. Switched Fault Diagnosis Approach for Industrial Processes based on Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Wang, Lin; Yang, Chunjie; Sun, Youxian; Pan, Yijun; An, Ruqiao

    2015-11-01

    Traditional fault diagnosis methods based on hidden Markov model (HMM) use a unified method for feature extraction, such as principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA). However, every method has its own limitations. For example, PCA cannot extract nonlinear relationships among process variables. So it is inappropriate to extract all features of variables by only one method, especially when data characteristics are very complex. This article proposes a switched feature extraction procedure using PCA and KPCA based on nonlinearity measure. By the proposed method, we are able to choose the most suitable feature extraction method, which could improve the accuracy of fault diagnosis. A simulation from the Tennessee Eastman (TE) process demonstrates that the proposed approach is superior to the traditional one based on HMM and could achieve more accurate classification of various process faults.

  5. A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

    NASA Astrophysics Data System (ADS)

    Al-Bugharbee, Hussein; Trendafilova, Irina

    2016-05-01

    This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pretreatment allows the use of a linear time invariant autoregressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasises the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal. The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process.

  6. Model-Based Diagnosis and Prognosis of a Water Recycling System

    NASA Technical Reports Server (NTRS)

    Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

    2013-01-01

    A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

  7. Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Roychoudhury, Indranil; Balaban, Edward; Saxena, Abhinav

    2010-01-01

    Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

  8. [Multi-diagnosis space models of As stress in rice based on hyperspectral indices].

    PubMed

    Cao, Shi; Liu, Xiang-Nan; Liu, Mu-Xia

    2010-10-01

    High arsenic content in rice can influence the chlorophyll, water content and structure in their leaves, reduce the rate of photosynthesis and change their spectral features. Multiple models for diagnosing As contamination in rice based on spectral parameters were studied. Sixty samples belonging to mature rice in three different areas were scanned by ASD field pro3 for optical data. Arsenic reference values were obtained by atomic absorption spectrometry. First, correlation analysis was used between 9 hyperspectral indices and As content in rice, and three indices (PSNDa, fWBI, SIPI) were extracted to diagnose As contamination in rice, which were respectively sensitive to chlorophyll, water content and structure of leaves, then took the three indices to form a diagnosis spectral indices space (PSNDa-fWBI, PSNDa-SIPI, fWBI-SIPI) of As stress in rice. Second, principal component analysis and independent component analysis were also applied in these 9 hyperspectral indices, and two principal components (F1, F2) and two independent components(ICA1, ICA2) were extracted. These four components (F1, F2, ICA1, ICA2) were all correlated with As content in rice, and composed another two diagnosis spaces (F1-F2, ICA1-ICA2) for predicting As contamination. And these spaces composed a multiple diagnosis space model which diagnosed As contamination in rice of test area from different level, and showed a good result. PMID:21229762

  9. Explanation Constraint Programming for Model-based Diagnosis of Engineered Systems

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Brownston, Lee; Burrows, Daniel

    2004-01-01

    We can expect to see an increase in the deployment of unmanned air and land vehicles for autonomous exploration of space. In order to maintain autonomous control of such systems, it is essential to track the current state of the system. When the system includes safety-critical components, failures or faults in the system must be diagnosed as quickly as possible, and their effects compensated for so that control and safety are maintained under a variety of fault conditions. The Livingstone fault diagnosis and recovery kernel and its temporal extension L2 are examples of model-based reasoning engines for health management. Livingstone has been shown to be effective, it is in demand, and it is being further developed. It was part of the successful Remote Agent demonstration on Deep Space One in 1999. It has been and is being utilized by several projects involving groups from various NASA centers, including the In Situ Propellant Production (ISPP) simulation at Kennedy Space Center, the X-34 and X-37 experimental reusable launch vehicle missions, Techsat-21, and advanced life support projects. Model-based and consistency-based diagnostic systems like Livingstone work only with discrete and finite domain models. When quantitative and continuous behaviors are involved, these are abstracted to discrete form using some mapping. This mapping from the quantitative domain to the qualitative domain is sometimes very involved and requires the design of highly sophisticated and complex monitors. We propose a diagnostic methodology that deals directly with quantitative models and behaviors, thereby mitigating the need for these sophisticated mappings. Our work brings together ideas from model-based diagnosis systems like Livingstone and concurrent constraint programming concepts. The system uses explanations derived from the propagation of quantitative constraints to generate conflicts. Fast conflict generation algorithms are used to generate and maintain multiple candidates

  10. Improved automated diagnosis of misfire in internal combustion engines based on simulation models

    NASA Astrophysics Data System (ADS)

    Chen, Jian; Bond Randall, Robert

    2015-12-01

    In this paper, a new advance in the application of Artificial Neural Networks (ANNs) to the automated diagnosis of misfires in Internal Combustion engines(IC engines) is detailed. The automated diagnostic system comprises three stages: fault detection, fault localization and fault severity identification. Particularly, in the severity identification stage, separate Multi-Layer Perceptron networks (MLPs) with saturating linear transfer functions were designed for individual speed conditions, so they could achieve finer classification. In order to obtain sufficient data for the network training, numerical simulation was used to simulate different ranges of misfires in the engine. The simulation models need to be updated and evaluated using experimental data, so a series of experiments were first carried out on the engine test rig to capture the vibration signals for both normal condition and with a range of misfires. Two methods were used for the misfire diagnosis: one is based on the torsional vibration signals of the crankshaft and the other on the angular acceleration signals (rotational motion) of the engine block. Following the signal processing of the experimental and simulation signals, the best features were selected as the inputs to ANN networks. The ANN systems were trained using only the simulated data and tested using real experimental cases, indicating that the simulation model can be used for a wider range of faults for which it can still be considered valid. The final results have shown that the diagnostic system based on simulation can efficiently diagnose misfire, including location and severity.

  11. Clustering diagnosis of rolling element bearing fault based on integrated Autoregressive/Autoregressive Conditional Heteroscedasticity model

    NASA Astrophysics Data System (ADS)

    Wang, Guofeng; Liu, Chang; Cui, Yinhu

    2012-09-01

    Feature extraction plays an important role in the clustering analysis. In this paper an integrated Autoregressive (AR)/Autoregressive Conditional Heteroscedasticity (ARCH) model is proposed to characterize the vibration signal and the model coefficients are adopted as feature vectors to realize clustering diagnosis of rolling element bearings. The main characteristic is that the AR item and ARCH item are interrelated with each other so that it can depict the excess kurtosis and volatility clustering information in the vibration signal more accurately in comparison with two-stage AR/ARCH model. To testify the correctness, four kinds of bearing signals are adopted for parametric modeling by using the integrated and two-stage AR/ARCH model. The variance analysis of the model coefficients shows that the integrated AR/ARCH model can get more concentrated distribution. Taking these coefficients as feature vectors, K means based clustering is utilized to realize the automatic classification of bearing fault status. The results show that the proposed method can get more accurate results in comparison with two-stage model and discrete wavelet decomposition.

  12. Applying Model-based Diagnosis to a Rapid Propellant Loading System

    NASA Technical Reports Server (NTRS)

    Goodrich, Charlie H.; Narasimhan, Sriram; Daigle, Matthew J.; Hatfield, Walter H.; Johnson, Robert G.

    2009-01-01

    The overall objective of the US Air Force Research Laboratory (AFRL) Rapid Propellant Loading (RPL) Program is to develop a launch vehicle, payload and ground support equipment that can support a rapid propellant load and launch within one hour. NASA Kennedy Space Center (KSC) has been funded by AFRL to develop hardware and software to demonstrate this capability. The key features of the software would be the ability to recognize and adapt to failures in the physical hardware components, advise operators of equipment faults and workarounds, and put the system in a safe configuration if unable to fly. In December 2008 NASA KSC and NASA Ames Research Center (ARC) demonstrated model based simulation and diagnosis capabilities for a scaled-down configuration of the RPL hardware. In this paper we present a description of the model-based technologies that were included as part of this demonstration and the results that were achieved. In continuation of this work we are currently testing the technologies on a simulation of the complete RPL system. Later in the year, when the RPL hardware is ready, we will be integrating these technologies with the real-time operation of the system to provide live state estimates. In future years we will be developing the capability to recover from faulty conditions via redundancy and reconfiguration.

  13. A Memory Based Model of Posttraumatic Stress Disorder: Evaluating Basic Assumptions Underlying the PTSD Diagnosis

    PubMed Central

    Rubin, David C.; Berntsen, Dorthe; Johansen, Malene Klindt

    2009-01-01

    In the mnemonic model of PTSD, the current memory of a negative event, not the event itself determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the DSM. The model accounts for important and reliable findings that are often inconsistent with the current diagnostic view and that have been neglected by theoretical accounts of the disorder, including the following observations. The diagnosis needs objective information about the trauma and peritraumatic emotions, but uses retrospective memory reports that can have substantial biases. Negative events and emotions that do not satisfy the current diagnostic criteria for a trauma can be followed by symptoms that would otherwise qualify for PTSD. Predisposing factors that affect the current memory have large effects on symptoms. The inability-to-recall-an-important-aspect-of-the-trauma symptom does not correlate with other symptoms. Loss or enhancement of the trauma memory affects PTSD symptoms in predictable ways. Special mechanisms that apply only to traumatic memories are not needed, increasing parsimony and the knowledge that can be applied to understanding PTSD. PMID:18954211

  14. Variable-Length Computerized Adaptive Testing Based on Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Hsu, Chia-Ling; Wang, Wen-Chung; Chen, Shu-Ying

    2013-01-01

    Interest in developing computerized adaptive testing (CAT) under cognitive diagnosis models (CDMs) has increased recently. CAT algorithms that use a fixed-length termination rule frequently lead to different degrees of measurement precision for different examinees. Fixed precision, in which the examinees receive the same degree of measurement…

  15. An Enhanced Learning Diagnosis Model Based on Concept-Effect Relationships with Multiple Knowledge Levels

    ERIC Educational Resources Information Center

    Chu, Hui-Chun; Hwang, Gwo-Jen; Huang, Yueh-Min

    2010-01-01

    Conventional testing systems usually give students a score as their test result, but do not show them how to improve their learning performance. Researchers have indicated that students would benefit more if individual learning guidance could be provided. However, most of the existing learning diagnosis models ignore the fact that one concept…

  16. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

    PubMed Central

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  17. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    PubMed

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  18. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    PubMed

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

  19. Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

    PubMed

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-05-05

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects.

  20. Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

    PubMed Central

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-01-01

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

  1. Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

    PubMed

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-01-01

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

  2. Completing fault models for abductive diagnosis

    SciTech Connect

    Knill, E. ); Cox, P.T.; Pietrzykowski, T. )

    1992-11-05

    In logic-based diagnosis, the consistency-based method is used to determine the possible sets of faulty devices. If the fault models of the devices are incomplete or nondeterministic, then this method does not necessarily yield abductive explanations of system behavior. Such explanations give additional information about faulty behavior and can be used for prediction. Unfortunately, system descriptions for the consistency-based method are often not suitable for abductive diagnosis. Methods for completing the fault models for abductive diagnosis have been suggested informally by Poole and by Cox et al. Here we formalize these methods by introducing a standard form for system descriptions. The properties of these methods are determined in relation to consistency-based diagnosis and compared to other ideas for integrating consistency-based and abductive diagnosis.

  3. A Memory-Based Model of Posttraumatic Stress Disorder: Evaluating Basic Assumptions Underlying the PTSD Diagnosis

    ERIC Educational Resources Information Center

    Rubin, David C.; Berntsen, Dorthe; Bohni, Malene Klindt

    2008-01-01

    In the mnemonic model of posttraumatic stress disorder (PTSD), the current memory of a negative event, not the event itself, determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the "Diagnostic and Statistical Manual of Mental Disorders" (4th ed., text rev.; American Psychiatric Association,…

  4. Combining FDI and AI approaches within causal-model-based diagnosis.

    PubMed

    Gentil, Sylviane; Montmain, Jacky; Combastel, Christophe

    2004-10-01

    This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.

  5. Immunity-based diagnosis for a motherboard.

    PubMed

    Shida, Haruki; Okamoto, Takeshi; Ishida, Yoshiteru

    2011-01-01

    We have utilized immunity-based diagnosis to detect abnormal behavior of components on a motherboard. The immunity-based diagnostic model monitors voltages of some components, CPU temperatures, and fan speeds. We simulated abnormal behaviors of some components on the motherboard, and we utilized the immunity-based diagnostic model to evaluate motherboard sensors in two experiments. These experiments showed that the immunity-based diagnostic model was an effective method for detecting abnormal behavior of components on the motherboard. PMID:22163857

  6. Power distribution system diagnosis with uncertainty information based on rough sets and clouds model

    NASA Astrophysics Data System (ADS)

    Sun, Qiuye; Zhang, Huaguang

    2006-11-01

    During the distribution system fault period, usually the explosive growth signals including fuzziness and randomness are too redundant to make right decision for the dispatcher. The volume of data with a few uncertainties overwhelms classic information systems in the distribution control center and exacerbates the existing knowledge acquisition process of expert systems. So intelligent methods must be developed to aid users in maintaining and using this abundance of information effectively. An important issue in distribution fault diagnosis system (DFDS) is to allow the discovered knowledge to be as close as possible to natural languages to satisfy user needs with tractability, and to offer DFDS robustness. At this junction, the paper describes a systematic approach for detecting superfluous data. The approach therefore could offer user both the opportunity to learn about the data and to validate the extracted knowledge. It is considered as a "white box" rather than a "black box" like in the case of neural network. The cloud theory is introduced and the mathematical description of cloud has effectively integrated the fuzziness and randomness of linguistic terms in a unified way. Based on it, a method of knowledge representation in DFDS is developed which bridges the gap between quantitative knowledge and qualitative knowledge. In relation to classical rough set, the cloud-rough method can deal with the uncertainty of the attribute and make a soft discretization for continuous ones (such as the current and the voltage). A novel approach, including discretization, attribute reduction, rule reliability computation and equipment reliability computation, is presented. The data redundancy is greatly reduced based on an integrated use of cloud theory and rough set theory. Illustrated with a power distribution DFDS shows the effectiveness and practicality of the proposed approach.

  7. Diagnosis of pulmonary hypertension from magnetic resonance imaging-based computational models and decision tree analysis.

    PubMed

    Lungu, Angela; Swift, Andrew J; Capener, David; Kiely, David; Hose, Rod; Wild, Jim M

    2016-06-01

    Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH. PMID:27252844

  8. Diagnosis of pulmonary hypertension from magnetic resonance imaging-based computational models and decision tree analysis.

    PubMed

    Lungu, Angela; Swift, Andrew J; Capener, David; Kiely, David; Hose, Rod; Wild, Jim M

    2016-06-01

    Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH.

  9. Diagnosis of pulmonary hypertension from magnetic resonance imaging–based computational models and decision tree analysis

    PubMed Central

    Swift, Andrew J.; Capener, David; Kiely, David; Hose, Rod; Wild, Jim M.

    2016-01-01

    Abstract Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH. PMID:27252844

  10. Knowledge-based diagnosis for aerospace systems

    NASA Technical Reports Server (NTRS)

    Atkinson, David J.

    1988-01-01

    The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

  11. Improving Distributed Diagnosis Through Structural Model Decomposition

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Daigle, Matthew John; Roychoudhury, Indranil; Biswas, Gautam; Koutsoukos, Xenofon; Pulido, Belarmino

    2011-01-01

    Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals computed by extending Possible Conflicts to build local event-based diagnosers based on global diagnosability analysis. The proposed approach is applied to a multitank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches.

  12. Physically-based modeling of speed sensors for fault diagnosis and fault tolerant control in wind turbines

    NASA Astrophysics Data System (ADS)

    Weber, Wolfgang; Jungjohann, Jonas; Schulte, Horst

    2014-12-01

    In this paper, a generic physically-based modeling framework for encoder type speed sensors is derived. The consideration takes into account the nominal fault-free and two most relevant fault cases. The advantage of this approach is a reconstruction of the output waveforms in dependence of the internal physical parameter changes which enables a more accurate diagnosis and identification of faulty incremental encoders i.a. in wind turbines. The objectives are to describe the effect of the tilt and eccentric of the encoder disk on the digital output signals and the influence of the accuracy of the speed measurement in wind turbines. Simulation results show the applicability and effectiveness of the proposed approach.

  13. Model-Based Fault Diagnosis: Performing Root Cause and Impact Analyses in Real Time

    NASA Technical Reports Server (NTRS)

    Figueroa, Jorge F.; Walker, Mark G.; Kapadia, Ravi; Morris, Jonathan

    2012-01-01

    Generic, object-oriented fault models, built according to causal-directed graph theory, have been integrated into an overall software architecture dedicated to monitoring and predicting the health of mission- critical systems. Processing over the generic fault models is triggered by event detection logic that is defined according to the specific functional requirements of the system and its components. Once triggered, the fault models provide an automated way for performing both upstream root cause analysis (RCA), and for predicting downstream effects or impact analysis. The methodology has been applied to integrated system health management (ISHM) implementations at NASA SSC's Rocket Engine Test Stands (RETS).

  14. The AIMAR recommendations for early diagnosis of chronic obstructive respiratory disease based on the WHO/GARD model*

    PubMed Central

    2014-01-01

    the Italian context; the document of the Agency for Regional Healthcare Services (AGE.NA.S) is a more suited compendium for consultation, and the recent joint statement on integrated COPD management of the three major Italian scientific Associations in the respiratory area together with the contribution of a Society of General Medicine deals prevalently with some critical issues (appropriateness of diagnosis, pharmacological treatment, rehabilitation, continuing care); also the document “Care Continuity: Chronic Obstructive Pulmonary Disease (COPD)” of the Global Alliance against chronic Respiratory Diseases (GARD)-Italy does not treat in depth the issue of early diagnosis. The present document – produced by the AIMAR (Interdisciplinary Association for Research in Lung Disease) Task Force for early diagnosis of chronic respiratory disease based on the WHO/GARD model and on available evidence and expertise –after a general examination of the main epidemiologic aspects, proposes to integrate the above-mentioned existing documents. In particular: a) it formally indicates on the basis of the available evidence the modalities and the instruments necessary for carrying out secondary prevention at the primary care level (a pro-active,‘case-finding’approach; assessment of the individual’s level of risk of COPD; use of short questionnaires for an initial screening based on symptoms; use of simple spirometry for the second level of screening); b) it identifies possible ways of including these activities within primary care practice; c) it places early diagnosis within the “systemic”, consequential management of chronic respiratory diseases, which will be briefly described with the aid of schemes taken from the Italian and international reference documents. PMID:25473523

  15. Final Progress Report on Model-Based Diagnosis of Soil Limitations to Forest Productivity

    SciTech Connect

    Luxmoore, R.J.

    2004-08-30

    This project was undertaken in support of the forest industry to link modeling of nutrients and productivity with field research to identify methods for enhancing soil quality and forest productivity and for alleviating soil limitations to sustainable forest productivity. The project consisted of a series of related tasks, including (1) simulation of changes in biomass and soil carbon with nitrogen fertilization, (2) development of spreadsheet modeling tools for soil nutrient availability and tree nutrient requirements, (3) additional modeling studies, and (4) evaluation of factors involved in the establishment and productivity of southern pine plantations in seasonally wet soils. This report also describes the two Web sites that were developed from the research to assist forest managers with nutrient management of Douglas-fir and loblolly pine plantations.

  16. Knowledge-based nursing diagnosis

    NASA Astrophysics Data System (ADS)

    Roy, Claudette; Hay, D. Robert

    1991-03-01

    Nursing diagnosis is an integral part of the nursing process and determines the interventions leading to outcomes for which the nurse is accountable. Diagnoses under the time constraints of modern nursing can benefit from a computer assist. A knowledge-based engineering approach was developed to address these problems. A number of problems were addressed during system design to make the system practical extended beyond capture of knowledge. The issues involved in implementing a professional knowledge base in a clinical setting are discussed. System functions, structure, interfaces, health care environment, and terminology and taxonomy are discussed. An integrated system concept from assessment through intervention and evaluation is outlined.

  17. Parameter estimation for uncertain systems based on fault diagnosis using Takagi-Sugeno model.

    PubMed

    Nagy-Kiss, A M; Schutz, G; Ragot, J

    2015-05-01

    The paper addresses a systematic procedure to deal with state and parameter uncertainty estimation for nonlinear time-varying systems. A robust observer with respect to states, inputs and perturbations is designed, using a Takagi-Sugeno (T-S) approach with unknown premise variables. Tools of the linear automatic to the nonlinear systems are applied, using the Linear Matrix Inequalities optimization. The observer estimates the uncertainties, the states and minimizes the effect of external disturbances on the estimation error. The uncertainties are modelled in a polynomial way which allows considering the uncertainty estimation as a fault detection problem. The residual sensitivity to faults while maintaining robustness according to a noise signal is handled by H∞/H- approach. The method performance is illustrated using the three-tank system. PMID:25677711

  18. A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

    NASA Astrophysics Data System (ADS)

    Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2016-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

  19. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.

    PubMed

    Zhang, B; Liang, X L; Gao, H Y; Ye, L S; Wang, Y G

    2016-05-13

    We evaluated the application of three machine learning algorithms, including logistic regression, support vector machine and back-propagation neural network, for diagnosing congenital heart disease and colorectal cancer. By inspecting related serum tumor marker levels in colorectal cancer patients and healthy subjects, early diagnosis models for colorectal cancer were built using three machine learning algorithms to assess their corresponding diagnostic values. Except for serum alpha-fetoprotein, the levels of 11 other serum markers of patients in the colorectal cancer group were higher than those in the benign colorectal cancer group (P < 0.05). The results of logistic regression analysis indicted that individual detection of serum carcinoembryonic antigens, CA199, CA242, CA125, and CA153 and their combined detection was effective for diagnosing colorectal cancer. Combined detection had a better diagnostic effect with a sensitivity of 94.2% and specificity of 97.7%; combining serum carcinoembryonic antigens, CA199, CA242, CA125, and CA153, with the support vector machine diagnosis model and back-propagation, a neural network diagnosis model was built with diagnostic accuracies of 82 and 75%, sensitivities of 85 and 80%, and specificities of 80 and 70%, respectively. Colorectal cancer diagnosis models based on the three machine learning algorithms showed high diagnostic value and can help obtain evidence for the early diagnosis of colorectal cancer.

  20. A Multicomponent Latent Trait Model for Diagnosis

    ERIC Educational Resources Information Center

    Embretson, Susan E.; Yang, Xiangdong

    2013-01-01

    This paper presents a noncompensatory latent trait model, the multicomponent latent trait model for diagnosis (MLTM-D), for cognitive diagnosis. In MLTM-D, a hierarchical relationship between components and attributes is specified to be applicable to permit diagnosis at two levels. MLTM-D is a generalization of the multicomponent latent trait…

  1. System monitoring and diagnosis with qualitative models

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1991-01-01

    A substantial foundation of tools for model-based reasoning with incomplete knowledge was developed: QSIM (a qualitative simulation program) and its extensions for qualitative simulation; Q2, Q3 and their successors for quantitative reasoning on a qualitative framework; and the CC (component-connection) and QPC (Qualitative Process Theory) model compilers for building QSIM QDE (qualitative differential equation) models starting from different ontological assumptions. Other model-compilers for QDE's, e.g., using bond graphs or compartmental models, have been developed elsewhere. These model-building tools will support automatic construction of qualitative models from physical specifications, and further research into selection of appropriate modeling viewpoints. For monitoring and diagnosis, plausible hypotheses are unified against observations to strengthen or refute the predicted behaviors. In MIMIC (Model Integration via Mesh Interpolation Coefficients), multiple hypothesized models of the system are tracked in parallel in order to reduce the 'missing model' problem. Each model begins as a qualitative model, and is unified with a priori quantitative knowledge and with the stream of incoming observational data. When the model/data unification yields a contradiction, the model is refuted. When there is no contradiction, the predictions of the model are progressively strengthened, for use in procedure planning and differential diagnosis. Only under a qualitative level of description can a finite set of models guarantee the complete coverage necessary for this performance. The results of this research are presented in several publications. Abstracts of these published papers are presented along with abtracts of papers representing work that was synergistic with the NASA grant but funded otherwise. These 28 papers include but are not limited to: 'Combined qualitative and numerical simulation with Q3'; 'Comparative analysis and qualitative integral representations

  2. Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling.

    PubMed

    Li, Fan; Upadhyaya, Belle R; Coffey, Lonnie A

    2009-04-01

    This paper presents an incipient fault diagnosis approach based on the Group Method of Data Handling (GMDH) technique. The GMDH algorithm provides a generic framework for characterizing the interrelationships among a set of process variables of fossil power plant sub-systems and is employed to generate estimates of important variables in a data-driven fashion. In this paper, ridge regression techniques are incorporated into the ordinary least squares (OLS) estimator to solve regression coefficients at each layer of the GMDH network. The fault diagnosis method is applied to feedwater heater leak detection with data from an operating coal-fired plant. The results demonstrate the proposed method is capable of providing an early warning to operators when a process fault or an equipment fault occurs in a fossil power plant. PMID:19084227

  3. Patient specific modeling of palpation-based prostate cancer diagnosis: effects of pelvic cavity anatomy and intrabladder pressure.

    PubMed

    Palacio-Torralba, Javier; Jiménez Aguilar, Elizabeth; Good, Daniel W; Hammer, Steven; McNeill, S Alan; Stewart, Grant D; Reuben, Robert L; Chen, Yuhang

    2016-01-01

    Computational modeling has become a successful tool for scientific advances including understanding the behavior of biological and biomedical systems as well as improving clinical practice. In most cases, only general models are used without taking into account patient-specific features. However, patient specificity has proven to be crucial in guiding clinical practice because of disastrous consequences that can arise should the model be inaccurate. This paper proposes a framework for the computational modeling applied to the example of the male pelvic cavity for the purpose of prostate cancer diagnostics using palpation. The effects of patient specific structural features on palpation response are studied in three selected patients with very different pathophysiological conditions whose pelvic cavities are reconstructed from MRI scans. In particular, the role of intrabladder pressure in the outcome of digital rectal examination is investigated with the objective of providing guidelines to practitioners to enhance the effectiveness of diagnosis. Furthermore, the presence of the pelvic bone in the model is assessed to determine the pathophysiological conditions in which it has to be modeled. The conclusions and suggestions of this work have potential use not only in clinical practice and also for biomechanical modeling where structural patient-specificity needs to be considered. © 2015 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.

  4. A PC based fault diagnosis expert system

    NASA Technical Reports Server (NTRS)

    Marsh, Christopher A.

    1990-01-01

    The Integrated Status Assessment (ISA) prototype expert system performs system level fault diagnosis using rules and models created by the user. The ISA evolved from concepts to a stand-alone demonstration prototype using OPS5 on a LISP Machine. The LISP based prototype was rewritten in C and the C Language Integrated Production System (CLIPS) to run on a Personal Computer (PC) and a graphics workstation. The ISA prototype has been used to demonstrate fault diagnosis functions of Space Station Freedom's Operation Management System (OMS). This paper describes the development of the ISA prototype from early concepts to the current PC/workstation version used today and describes future areas of development for the prototype.

  5. A theoretical model for the development of a diagnosis-based clinical decision rule for the management of patients with spinal pain

    PubMed Central

    Murphy, Donald R; Hurwitz, Eric L

    2007-01-01

    Background Spinal pain is a common problem, and disability related to spinal pain has great consequence in terms of human suffering, medical costs and costs to society. The traditional approach to the non-surgical management of patients with spinal pain, as well as to research in spinal pain, has been such that the type of treatment any given patient receives is determined more by what type of practitioner he or she sees, rather than by diagnosis. Furthermore, determination of treatment depends more on the type of practitioner than by the needs of the patient. Much needed is an approach to clinical management and research that allows clinicians to base treatment decisions on a reliable and valid diagnostic strategy leading to treatment choices that result in demonstrable outcomes in terms of pain relief and functional improvement. The challenges of diagnosis in patients with spinal pain, however, are that spinal pain is often multifactorial, the factors involved are wide ranging, and for most of these factors there exist no definitive objective tests. Discussion The theoretical model of a diagnosis-based clinical decision rule has been developed that may provide clinicians with an approach to non-surgical spine pain patients that allows for specific treatment decisions based on a specific diagnosis. This is not a classification scheme, but a thought process that attempts to identify most important features present in each individual patient. Presented here is a description of the proposed approach, in which reliable and valid assessment procedures are used to arrive at a working diagnosis which considers the disparate factors contributing to spinal pain. Treatment decisions are based on the diagnosis and the outcome of treatment can be measured. Summary In this paper, the theoretical model of a proposed diagnosis-based clinical decision rule is presented. In a subsequent manuscript, the current evidence for the approach will be systematically reviewed, and we will

  6. Developing a semantic web model for medical differential diagnosis recommendation.

    PubMed

    Mohammed, Osama; Benlamri, Rachid

    2014-10-01

    In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.

  7. Developing a semantic web model for medical differential diagnosis recommendation.

    PubMed

    Mohammed, Osama; Benlamri, Rachid

    2014-10-01

    In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results. PMID:25178271

  8. Development of a model based scoring system for diagnosis of canine disseminated intravascular coagulation with independent assessment of sensitivity and specificity.

    PubMed

    Wiinberg, Bo; Jensen, Asger L; Johansson, Pär I; Kjelgaard-Hansen, Mads; Rozanski, Elizabeth; Tranholm, Mikael; Kristensen, Annemarie T

    2010-09-01

    A template for a scoring system for disseminated intravascular coagulation (DIC) in humans has been proposed by the International Society on Thrombosis and Haemostasis (ISTH). The objective of this study was to develop and validate a similar objective scoring system based on generally available coagulation tests for the diagnosis of DIC in dogs. To develop the scoring system, 100 dogs consecutively admitted to an intensive care unit (ICU) with diseases predisposing for DIC were enrolled prospectively (group A). The validation involved 50 dogs consecutively diagnosed with diseases predisposing for DIC, admitted to a different ICU (group B). Citrated blood samples were collected daily during hospitalisation and diagnosis of DIC was based on the expert evaluation of an extended coagulation panel. A multiple logistic regression model was developed in group A for DIC diagnosis. The integrity and diagnostic accuracy of the model was subsequently evaluated in a separate prospective study at a different ICU (group B) and was carried out according to The Standards for Reporting of Diagnostic Accuracy (STARD) criteria. Thirty-seven dogs were excluded from group A and four from group B due to missing data. Based on expert opinion, 23/63 dogs (37%) had DIC. The final multiple logistic regression model was based on activated partial thromboplastin time, prothrombin time, D-Dimer and fibrinogen. The model had a diagnostic sensitivity and specificity of 90.9% and 90.0%, respectively. The diagnostic accuracy of the model was sustained by prospective evaluation in group B (sensitivity 83.3%, specificity 77.3%). Based on commonly used, plasma-based coagulation assays, it was possible to design an objective diagnostic scoring system for canine DIC with a high sensitivity and specificity.

  9. Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals

    NASA Astrophysics Data System (ADS)

    Silveira, Landulfo; Silveira, Fabrício Luiz; Bodanese, Benito; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu T.

    2012-07-01

    Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

  10. Relative and Absolute Fit Evaluation in Cognitive Diagnosis Modeling

    ERIC Educational Resources Information Center

    Chen, Jinsong; de la Torre, Jimmy; Zhang, Zao

    2013-01-01

    As with any psychometric models, the validity of inferences from cognitive diagnosis models (CDMs) determines the extent to which these models can be useful. For inferences from CDMs to be valid, it is crucial that the fit of the model to the data is ascertained. Based on a simulation study, this study investigated the sensitivity of various fit…

  11. An architecture for the development of real-time fault diagnosis systems using model-based reasoning

    NASA Technical Reports Server (NTRS)

    Hall, Gardiner A.; Schuetzle, James; Lavallee, David; Gupta, Uday

    1992-01-01

    Presented here is an architecture for implementing real-time telemetry based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for offline entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed offline by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.

  12. Final Report ''Model-Based Approach to Soft-Sensing and Diagnosis for Control of a Continuous Digester''

    SciTech Connect

    Francis J. Doyle III

    2003-06-01

    The goal of the work was to develop and demonstrate computing based modeling and control methodologies that will facilitate integrated operations on the continuous pulp digester. The technical work required to achieve these goals included development of efficient methods for soft-sensing using fundamental models, integration of fault monitoring and control algorithms, and the development of computationally feasible formulations of model predictive control for profile management in the digester. The developed operational methodology for digester grade transition control was benchmarked against an industrial design in cooperation with our collaborators at Weyerhaeuser and Westvaco.

  13. Diagnosis of Parkinson’s disease on the basis of clinical–genetic classification: a population-based modelling study

    PubMed Central

    Nalls, Mike A.; McLean, Cory Y.; Rick, Jacqueline; Eberly, Shirley; Hutten, Samantha J.; Gwinn, Katrina; Sutherland, Margaret; Martinez, Maria; Heutink, Peter; Williams, Nigel; Hardy, John; Gasser, Thomas; Brice, Alexis; Price, T. Ryan; Nicolas, Aude; Keller, Margaux F.; Molony, Cliona; Gibbs, J. Raphael; Chen-Plotkin, Alice; Suh, Eunran; Letson, Christopher; Fiandaca, Massimo S.; Mapstone, Mark; Federoff, Howard J.; Noyce, Alastair J; Morris, Huw; Van Deerlin, Vivianna M.; Weintraub, Daniel; Zabetian, Cyrus; Hernandez, Dena G.; Lesage, Suzanne; Mullins, Meghan; Conley, Emily Drabant; Northover, Carrie; Frasier, Mark; Marek, Ken; Day-Williams, Aaron G.; Stone, David J.; Ioannidis, John P. A.; Singleton, Andrew B.

    2015-01-01

    Background Accurate diagnosis and early detection of complex disease has the potential to be of enormous benefit to clinical trialists, patients, and researchers alike. We sought to create a non-invasive, low-cost, and accurate classification model for diagnosing Parkinson’s disease risk to serve as a basis for future disease prediction studies in prospective longitudinal cohorts. Methods We developed a simple disease classifying model within 367 patients with Parkinson’s disease and phenotypically typical imaging data and 165 controls without neurological disease of the Parkinson’s Progression Marker Initiative (PPMI) study. Olfactory function, genetic risk, family history of PD, age and gender were algorithmically selected as significant contributors to our classifying model. This model was developed using the PPMI study then tested in 825 patients with Parkinson’s disease and 261 controls from five independent studies with varying recruitment strategies and designs including the Parkinson’s Disease Biomarkers Program (PDBP), Parkinson’s Associated Risk Study (PARS), 23andMe, Longitudinal and Biomarker Study in PD (LABS-PD), and Morris K. Udall Parkinson’s Disease Research Center of Excellence (Penn-Udall). Findings Our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0.923 (95% CI = 0.900 – 0.946) with high sensitivity (0.834, 95% CI = 0.711 – 0.883) and specificity (0.903, 95% CI = 0.824 – 0.946) in PPMI at its optimal AUC threshold (0.655). The model is also well-calibrated with all Hosmer-Lemeshow simulations suggesting that when parsed into random subgroups, the actual data mirrors that of the larger expected data, demonstrating that our model is robust and fits well. Likewise external validation shows excellent classification of PD with AUCs of 0.894 in PDBP, 0.998 in PARS, 0.955 in 23andMe, 0.929 in LABS-PD, and 0.939 in Penn-Udall. Additionally, when our model

  14. An observer based approach for achieving fault diagnosis and fault tolerant control of systems modeled as hybrid Petri nets.

    PubMed

    Renganathan, K; Bhaskar, VidhyaCharan

    2011-07-01

    In this paper, we propose an approach for achieving detection and identification of faults, and provide fault tolerant control for systems that are modeled using timed hybrid Petri nets. For this purpose, an observer based technique is adopted which is useful in detection of faults, such as sensor faults, actuator faults, signal conditioning faults, etc. The concepts of estimation, reachability and diagnosability have been considered for analyzing faulty behaviors, and based on the detected faults, different schemes are proposed for achieving fault tolerant control using optimization techniques. These concepts are applied to a typical three tank system and numerical results are obtained.

  15. A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

    2015-01-01

    Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

  16. A Doppler transient model based on the laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis.

    PubMed

    Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W

    2013-11-18

    The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.

  17. Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Palmer, Gregory M.; Zhu, Changfang; Breslin, Tara M.; Xu, Fushen; Gilchrist, Kennedy W.; Ramanujam, Nirmala

    2006-02-01

    The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82% and 92%, respectively.

  18. A Doppler transient model based on the laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis.

    PubMed

    Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W

    2013-01-01

    The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully. PMID:24253191

  19. Hierarchical Item Response Models for Cognitive Diagnosis

    ERIC Educational Resources Information Center

    Hansen, Mark Patrick

    2013-01-01

    Cognitive diagnosis models (see, e.g., Rupp, Templin, & Henson, 2010) have received increasing attention within educational and psychological measurement. The popularity of these models may be largely due to their perceived ability to provide useful information concerning both examinees (classifying them according to their attribute profiles)…

  20. Establishment of Simple and Routine Methods in Early Diagnosis of Gentamicin-Induced Kidney Injury Based on a Rat Model.

    PubMed

    Liu, Cuiyan; Kang, Youxi; Zhang, Huiqin; Zhu, Long; Yu, Hai; Han, Chunyang

    2016-01-01

    The changes in biomarkers of gentamycin- (GM-) induced kidney injury have been studied by using simple and routine methods and also assessed the efficacy and utility of these routine biomarkers in early diagnosis. Eighty Sprague-Dawley (SD) rats were randomly divided into 4 groups: three experimental groups treated with different GM dosages (4, 20, and 100 mg·kg(-1)) and a control group. The experimental groups were given intramuscular GM injections once daily for 14 days, and the control group was given intramuscular sterile water. Blood and urine samples were collected on treatment days 1, 3, 7, and 14 to test for total protein (TP), albumin (ALB), blood urea nitrogen (BUN), creatinine (CRE), uric acid (UA), pH, specific gravity (SG), proteins (PRO), and cells in urinary sediment. Histopathology and kidney coefficient were performed on excised kidney specimens. The result indicated that serum CRE, BUN, and TP, urine PRO, and urinary hyaline casts and low-transitional epithelium showed an immediate and highly sensitive response to kidney injury, and the combined diagnosis with the above methods could be used in early diagnosis. Particularly, the process of the test was simple and quick, no special equipment, so it is more suit for primary medical institution. PMID:27631009

  1. Establishment of Simple and Routine Methods in Early Diagnosis of Gentamicin-Induced Kidney Injury Based on a Rat Model

    PubMed Central

    Kang, Youxi; Zhang, Huiqin; Yu, Hai

    2016-01-01

    The changes in biomarkers of gentamycin- (GM-) induced kidney injury have been studied by using simple and routine methods and also assessed the efficacy and utility of these routine biomarkers in early diagnosis. Eighty Sprague-Dawley (SD) rats were randomly divided into 4 groups: three experimental groups treated with different GM dosages (4, 20, and 100 mg·kg−1) and a control group. The experimental groups were given intramuscular GM injections once daily for 14 days, and the control group was given intramuscular sterile water. Blood and urine samples were collected on treatment days 1, 3, 7, and 14 to test for total protein (TP), albumin (ALB), blood urea nitrogen (BUN), creatinine (CRE), uric acid (UA), pH, specific gravity (SG), proteins (PRO), and cells in urinary sediment. Histopathology and kidney coefficient were performed on excised kidney specimens. The result indicated that serum CRE, BUN, and TP, urine PRO, and urinary hyaline casts and low-transitional epithelium showed an immediate and highly sensitive response to kidney injury, and the combined diagnosis with the above methods could be used in early diagnosis. Particularly, the process of the test was simple and quick, no special equipment, so it is more suit for primary medical institution. PMID:27631009

  2. Establishment of Simple and Routine Methods in Early Diagnosis of Gentamicin-Induced Kidney Injury Based on a Rat Model

    PubMed Central

    Kang, Youxi; Zhang, Huiqin; Yu, Hai

    2016-01-01

    The changes in biomarkers of gentamycin- (GM-) induced kidney injury have been studied by using simple and routine methods and also assessed the efficacy and utility of these routine biomarkers in early diagnosis. Eighty Sprague-Dawley (SD) rats were randomly divided into 4 groups: three experimental groups treated with different GM dosages (4, 20, and 100 mg·kg−1) and a control group. The experimental groups were given intramuscular GM injections once daily for 14 days, and the control group was given intramuscular sterile water. Blood and urine samples were collected on treatment days 1, 3, 7, and 14 to test for total protein (TP), albumin (ALB), blood urea nitrogen (BUN), creatinine (CRE), uric acid (UA), pH, specific gravity (SG), proteins (PRO), and cells in urinary sediment. Histopathology and kidney coefficient were performed on excised kidney specimens. The result indicated that serum CRE, BUN, and TP, urine PRO, and urinary hyaline casts and low-transitional epithelium showed an immediate and highly sensitive response to kidney injury, and the combined diagnosis with the above methods could be used in early diagnosis. Particularly, the process of the test was simple and quick, no special equipment, so it is more suit for primary medical institution.

  3. Calibrating perceived understanding and competency in probability concepts: A diagnosis of learning difficulties based on Rasch probabilistic model

    NASA Astrophysics Data System (ADS)

    Mahmud, Zamalia; Porter, Anne; Salikin, Masniyati; Ghani, Nor Azura Md

    2015-12-01

    Students' understanding of probability concepts have been investigated from various different perspectives. Competency on the other hand is often measured separately in the form of test structure. This study was set out to show that perceived understanding and competency can be calibrated and assessed together using Rasch measurement tools. Forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW have volunteered to participate in the study. Rasch measurement which is based on a probabilistic model is used to calibrate the responses from two survey instruments and investigate the interactions between them. Data were captured from the e-learning platform Moodle where students provided their responses through an online quiz. The study shows that majority of the students perceived little understanding about conditional and independent events prior to learning about it but tend to demonstrate a slightly higher competency level afterward. Based on the Rasch map, there is indication of some increase in learning and knowledge about some probability concepts at the end of the two weeks lessons on probability concepts.

  4. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, L.J.; Keller, P.E.

    1997-10-28

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

  5. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  6. The Fuzzy Model for Diagnosis of Animal Disease

    NASA Astrophysics Data System (ADS)

    Jianhua, Xiao; Luyi, Shi; Yu, Zhang; Li, Gao; Honggang, Fan; Haikun, Ma; Hongbin, Wang

    The knowledge of animal disease diagnosis was fuzzy; the fuzzy model can imitate the character of clinical diagnosis for veterinary. The fuzzy model of disease, the methods for class the disease group of differential diagnosis and the fuzzy diagnosis model were discussed in this paper.

  7. In vivo imaging using a VEGF-based near-infrared fluorescent probe for early cancer diagnosis in the AOM-treated mouse model

    NASA Astrophysics Data System (ADS)

    Winkler, Amy M.; Rice, Photini F. S.; Weichsel, Jan; Backer, Marina V.; Backer, Joseph M.; Barton, Jennifer K.

    2009-02-01

    Strong vascular endothelial growth factor (VEGF) receptor expression has been found at the sites of angiogenesis, particularly in tumor growth areas. An increase in VEGF receptor-2 is associated with colon cancer progression. The in vivo detection of VEGF receptor is of interest for the purposes of studying carcinogenesis, the efficacy of chemopreventive and therapeutic agents, clinical diagnosis, and therapeutic monitoring. In this study, a novel single chain (sc) VEGF-based molecular probe is utilized in the AOM-treated mouse model of colorectal cancer to study delivery route and specificity for disease. The probe was constructed by site-specific conjugation of a near-infrared dye, Cy5.5, to scVEGF and detected in vivo with a dual-modality optical coherence tomography / laser-induced fluorescence (OCT/LIF) endoscopic system. The LIF excitation source was a 633 nm He:Ne laser and red/near-infrared fluorescence was detected with a spectrometer. OCT was used to obtain two-dimensional longitudinal tomograms at eight rotations in the distal colon. Fluorescence emission levels were correlated with OCT-detected disease in vivo and H&E stained histology slides ex vivo. Specificity for disease was found to be highly dependent on the delivery route. Intravenous injection resulted in poor specificity due to many extra-colon confounders, while colon lavage eliminated most of these. High fluorescence emission intensity was correlated with tumor presence as detected using OCT. Results suggest potential for clinical use to facilitate earlier diagnosis of cancer.

  8. Surgical Intervention for Masticatory Muscle Tendon-Aponeurosis Hyperplasia Based on the Diagnosis Using the Four-Dimensional Muscle Model

    PubMed Central

    Nakaoka, Kazutoshi; Hamada, Yoshiki; Nakatani, Hayaki; Shigeta, Yuko; Hirai, Shinya; Ikawa, Tomoko; Mishima, Akira; Ogawa, Takumi

    2015-01-01

    Objectives: The surgical target of Masticatory muscle tendon-aponeurosis hyperplasia (MMTAH) is the masseter or temporal muscle. In our clinic, the 4-dimentional muscle model (4DMM) has been used to decide if we should approach to the masseter or temporal muscle. The aim of this study is validate the clinical usefulness of 4DMM on the basis of the surgical results. Methods: The 4DMM was constructed from the digital data of 3D-CT and 4-dimentional mandibular movements of the patients. It made us to able to visually observe the expansion rate of masticatory muscles at maximum mouth opening comparing to their length at closed mouth position. Fifteen patients were applied the 4DMM before the surgical treatment and 2 healthy volunteers were enrolled as control group. Results: The expansion rate of temporal muscle at the maximum mouth opening in the patient group was significantly less than that in the control group (P < 0.05). On the other hand, the masseter muscles of all patients were expanded as same as the control group. Therefore the main cause of limitation of mouth-opening was suggested to be a contracture of the temporal muscle. Consequently, we performed successful bilateral coronoidectomy with no surgical intervention to the masseter muscles in all patients. Conclusion: The present 4DMM would be valuable modality to decide the target muscle of surgical treatment for patients with MMTAH. In this pathology, contracture of the temporal muscle seems to be main cause of limited mouth opening. PMID:26352365

  9. Crystallographic Model Validation: from Diagnosis to Healing

    PubMed Central

    Richardson, Jane S.; Prisant, Michael G.; Richardson, David C.

    2013-01-01

    Model validation has evolved from a passive final gatekeeping step to an ongoing diagnosis and healing process that enables significant improvement of accuracy. A recent phase of active development was spurred by the worldwide Protein Data Bank requiring data deposition and establishing Validation Task Force committees, by strong growth in high-quality reference data, by new speed and ease of computations, and by an upswing of interest in large molecular machines and structural ensembles. Progress includes automated correction methods, concise and user-friendly validation reports for referees and on the PDB websites, extension of error correction to RNA and error diagnosis to ligands, carbohydrates, and membrane proteins, and a good start on better methods for low resolution and for multiple conformations. PMID:24064406

  10. Evaluation of Model Fit in Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Hu, Jinxiang; Miller, M. David; Huggins-Manley, Anne Corinne; Chen, Yi-Hsin

    2016-01-01

    Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC),…

  11. Biotechnology-based allergy diagnosis and vaccination.

    PubMed

    Bhalla, Prem L; Singh, Mohan B

    2008-03-01

    The diagnosis and immunotherapy currently applied to allergic diseases involve the use of crude extracts of the allergen source without defining the allergy-eliciting molecule(s). Advances in recombinant DNA technology have made identification, cloning, expression and epitope mapping of clinically significant allergens possible. Recombinant allergens that retain the immunological features of natural allergens form the basis of accurate protein-chip-based methods for diagnosing allergic conditions. The ability to produce rationally designed hypoallergenic forms of allergens is leading to the development of novel and safe forms of allergy vaccines with improved efficacy. The initial clinical tests on recombinant-allergen-based vaccine preparations have provided positive results, and ongoing developments in areas such as alternative routes of vaccine delivery will enhance patient compliance.

  12. Fitting Data to Model: Structural Equation Modeling Diagnosis Using Two Scatter Plots

    ERIC Educational Resources Information Center

    Yuan, Ke-Hai; Hayashi, Kentaro

    2010-01-01

    This article introduces two simple scatter plots for model diagnosis in structural equation modeling. One plot contrasts a residual-based M-distance of the structural model with the M-distance for the factor score. It contains information on outliers, good leverage observations, bad leverage observations, and normal cases. The other plot contrasts…

  13. Wireless laptop-based phonocardiograph and diagnosis

    PubMed Central

    2015-01-01

    Auscultation is used to evaluate heart health, and can indicate when it’s needed to refer a patient to a cardiologist. Advanced phonocardiograph (PCG) signal processing algorithms are developed to assist the physician in the initial diagnosis but they are primarily designed and demonstrated with research quality equipment. Therefore, there is a need to demonstrate the applicability of those techniques with consumer grade instrument. Furthermore, routine monitoring would benefit from a wireless PCG sensor that allows continuous monitoring of cardiac signals of patients in physical activity, e.g., treadmill or weight exercise. In this work, a low-cost portable and wireless healthcare monitoring system based on PCG signal is implemented to validate and evaluate the most advanced algorithms. Off-the-shelf electronics and a notebook PC are used with MATLAB codes to record and analyze PCG signals which are collected with a notebook computer in tethered and wireless mode. Physiological parameters based on the S1 and S2 signals and MATLAB codes are demonstrated. While the prototype is based on MATLAB, the later is not an absolute requirement. PMID:26339555

  14. Measurement of Psychological Disorders Using Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Templin, Jonathan L.; Henson, Robert A.

    2006-01-01

    Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article…

  15. 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

  16. Computer modeling of lung cancer diagnosis-to-treatment process

    PubMed Central

    Ju, Feng; Lee, Hyo Kyung; Osarogiagbon, Raymond U.; Yu, Xinhua; Faris, Nick

    2015-01-01

    We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed. PMID:26380181

  17. FDSAC-SPICE: fault diagnosis software for analog circuit based on SPICE simulation

    NASA Astrophysics Data System (ADS)

    Cao, Yiqin; Cen, Zhao-Hui; Wei, Jiao-Long

    2009-12-01

    This paper presents a novel fault diagnosis software (called FDSAC-SPICE) based on SPICE simulator for analog circuits. Four important techniques in AFDS-SPICE, including visual user-interface(VUI), component modeling and fault modeling (CMFM), fault injection and fault simulation (FIFS), fault dictionary and fault diagnosis (FDFD), greatly increase design-for-test and diagnosis efficiency of analog circuit by building a fault modeling-injection-simulationdiagnosis environment to get prior fault knowledge of target circuit. AFDS-SPICE also generates accurate fault coverage statistics that are tied to the circuit specifications. With employing a dictionary diagnosis method based on node-signalcharacters and regular BPNN algorithm, more accurate and effective diagnosis results are available for analog circuit with tolerance.

  18. [Endocrine diagnosis in puberty--pathophysiologic bases].

    PubMed

    Girard, J

    1994-05-01

    Puberty is characterized by activation of the maturing gonads and by the thus started increased secretion of sexual steroids. Consequences are the appearance of secondary signs of puberty sensu strictori, i. e. the development of breasts in girls, the increase of testicle volume in boys, often followed by growing pubic hair, axillary hair, menarche or laryngeal growth (puberty vocal change) respectively. The most important accompanying symptom is the spurt of growth starting around 12 to 18 months after the onset of the development of the secondary pubertal signs. From the time sequence of the development and the possible delays, valuable diagnostic hints can be gained, giving rise to a more precise analysis of the hormonal phenomena of adolescence. In cases of pubertas tarda a primary malfunction must be differentiated from secondary hypogonadotropic functional defect. The syndromes should be classified correctly according to their etiology. The most frequent diagnosis is that of a simply delayed puberty. Acne, hypertrichosis, hirsutism are concomitant phenomena of puberty development which can indicate a hormonal imbalance (differential diagnosis AGS, ovarian hyperandrogeny). The swelling of breasts in boys (gynecomastia) is a common transitory phenomenon in male adolescence (DD, tumor of the gonads or Klinefelter syndrome). Interesting considerations of differential diagnosis apply also to the assessment of the enlargement of the thyroid gland in puberty, which affects more often girls than boys. PMID:8016754

  19. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2011-12-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  20. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2012-01-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  1. Knowledge-based fault diagnosis system for refuse collection vehicle

    SciTech Connect

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-05-15

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  2. Knowledge-based fault diagnosis system for refuse collection vehicle

    NASA Astrophysics Data System (ADS)

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-05-01

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  3. Secure Medical Diagnosis Using Rule Based Mining

    NASA Astrophysics Data System (ADS)

    Saleem Durai, M. A.; Sriman Narayana Iyengar, N. Ch.

    Security is the governing dynamics of all walks of life. Here we propose a secured medical diagnosis system. Certain specific rules are specified implicitly by the designer of the expert system and then symptoms for the diseases are obtained from the users and by using the pre defined confidence and support values we extract a threshold value which is used to conclude on a particular disease and the stage using Rule Mining. "THINK" CAPTCHA mechanism is used to distinguish between the human and the robots thereby eliminating the robots and preventing them from creating fake accounts and spam's. A novel image encryption mechanism is designed using genetic algorithm to encrypt the medical images thereby storing and sending the image data in a secured manner.

  4. Automated diagnosis of data-model conflicts using metadata.

    PubMed

    Chen, R O; Altman, R B

    1999-01-01

    The authors describe a methodology for helping computational biologists diagnose discrepancies they encounter between experimental data and the predictions of scientific models. The authors call these discrepancies data-model conflicts. They have built a prototype system to help scientists resolve these conflicts in a more systematic, evidence-based manner. In computational biology, data-model conflicts are the result of complex computations in which data and models are transformed and evaluated. Increasingly, the data, models, and tools employed in these computations come from diverse and distributed resources, contributing to a widening gap between the scientist and the original context in which these resources were produced. This contextual rift can contribute to the misuse of scientific data or tools and amplifies the problem of diagnosing data-model conflicts. The authors' hypothesis is that systematic collection of metadata about a computational process can help bridge the contextual rift and provide information for supporting automated diagnosis of these conflicts. The methodology involves three major steps. First, the authors decompose the data-model evaluation process into abstract functional components. Next, they use this process decomposition to enumerate the possible causes of the data-model conflict and direct the acquisition of diagnostically relevant metadata. Finally, they use evidence statically and dynamically generated from the metadata collected to identify the most likely causes of the given conflict. They describe how these methods are implemented in a knowledge-based system called GRENDEL and show how GRENDEL can be used to help diagnose conflicts between experimental data and computationally built structural models of the 30S ribosomal subunit. PMID:10495098

  5. Diagnosis of Photochemical Ozone Production Rates and Limiting Factors based on Observation-based Modeling Approach over East Asia: Impact of Radical Chemistry Mechanism and Ozone-Control Implications

    NASA Astrophysics Data System (ADS)

    Kanaya, Y.

    2015-12-01

    Growth of tropospheric ozone, causing health and climate impacts, is concerned over East Asia, because emissions of precursors have dramatically increased. Photochemical production rates of ozone and limiting factors, primarily studied for urban locations, have been poorly assessed within a perspective of regional-scale air pollution over East Asia. We performed comprehensive observations of ozone precursors at several locations with regional representativeness and made such assessment based on the observation-based modeling approach. Here, diagnosis at Fukue Island (32.75°N, 128.68°E) remotely located in western Japan (May 2009) is highlighted, where the highest 10% of hourly ozone concentrations reached 72‒118 ppb during May influenced by Asian continental outflow. The average in-situ ozone production rate was estimated to be 6.8 ppb per day, suggesting that in-travel production was still active, while larger buildup must have occurred beforehand. Information on the chemical status of the air mass arriving in Japan is important, because it affects how further ozone production occurs after precursor addition from Japanese domestic emissions. The main limiting factor of ozone production was usually NOx, suggesting that domestic NOx emission control is important in reducing further ozone production and the incidence of warning issuance (>120 ppb). VOCs also increased the ozone production rate, and occasionally (14% of time) became dominant. This analysis implies that the VOC reduction legislation recently enacted should be effective. The uncertainty in the radical chemistry mechanism governing ozone production had a non-negligible impact, but the main conclusion relevant to policy was not altered. When chain termination was augmented by HO2-H2O + NO/NO2 reactions and by heterogeneous loss of HO2 on aerosol particle surfaces, the daily ozone production rate decreased by <24%, and the fraction of hours when the VOC-limited condition occurred varied from 14% to 13

  6. Metal-based nanosystems for diagnosis.

    PubMed

    Popescu, Roxana Cristina; Fufă, Mariana Oana Mihaela; Grumezescu, Alexandru Mihai

    2015-01-01

    The impressive diversity related to etiologic factors and the distinctive genetic and immunological behavior attained by various conditions represent the fundamental reasons for high-rated inefficient and eventual hazardous strategies entailed by conventional healthcare practice. Thanks to the tremendous progress reported in nanotechnology during the last decades, various unconventional and promising strategies have been successfully developed and examined with respect to potential genuine biomedical applications. Given the amazing possibility to manipulate matter at a molecular and atomic level and the incessant need to design and implement personalized therapies, various nanosized systems have thus been engineered. Among the newly developed nanomaterials, metallic nanoparticles have gain attention during the intense biomedical research activity, thanks to their peculiar size-conditioned properties. An efficient therapeutic strategy begins with an accurate diagnosis result, so the immediate requirement of such specific detection tools is conspicuous. The use of silver and gold in day-to-day activities is acknowledged since ancient times, but the novel technological opportunities extended their particular applications towards personalized medicine. It is worthy to mention that the unexpected nanodimension-related features of the aforementioned noble metals strongly recommend them for a large number of current applications in nanomedicine, including novel and specific metallic nanostructures used in diagnostics.

  7. Higher-Order Latent Trait Models for Cognitive Diagnosis

    ERIC Educational Resources Information Center

    de la Torre, Jimmy; Douglas, Jeffrey A.

    2004-01-01

    Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative…

  8. An Event-Based Approach to Distributed Diagnosis of Continuous Systems

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

    2010-01-01

    Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

  9. Epiretinal membrane: optical coherence tomography-based diagnosis and classification

    PubMed Central

    Stevenson, William; Prospero Ponce, Claudia M; Agarwal, Daniel R; Gelman, Rachel; Christoforidis, John B

    2016-01-01

    Epiretinal membrane (ERM) is a disorder of the vitreomacular interface characterized by symptoms of decreased visual acuity and metamorphopsia. The diagnosis and classification of ERM has traditionally been based on clinical examination findings. However, modern optical coherence tomography (OCT) has proven to be more sensitive than clinical examination for the diagnosis of ERM. Furthermore, OCT-derived findings, such as central foveal thickness and inner segment ellipsoid band integrity, have shown clinical relevance in the setting of ERM. To date, no OCT-based ERM classification scheme has been widely accepted for use in clinical practice and investigation. Herein, we review the pathogenesis, diagnosis, and classification of ERMs and propose an OCT-based ERM classification system. PMID:27099458

  10. Skin image illumination modeling and chromophore identification for melanoma diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Zhao; Zerubia, Josiane

    2015-05-01

    The presence of illumination variation in dermatological images has a negative impact on the automatic detection and analysis of cutaneous lesions. This paper proposes a new illumination modeling and chromophore identification method to correct lighting variation in skin lesion images, as well as to extract melanin and hemoglobin concentrations of human skin, based on an adaptive bilateral decomposition and a weighted polynomial curve fitting, with the knowledge of a multi-layered skin model. Different from state-of-the-art approaches based on the Lambert law, the proposed method, considering both specular reflection and diffuse reflection of the skin, enables us to address highlight and strong shading effects usually existing in skin color images captured in an uncontrolled environment. The derived melanin and hemoglobin indices, directly relating to the pathological tissue conditions, tend to be less influenced by external imaging factors and are more efficient in describing pigmentation distributions. Experiments show that the proposed method gave better visual results and superior lesion segmentation, when compared to two other illumination correction algorithms, both designed specifically for dermatological images. For computer-aided diagnosis of melanoma, sensitivity achieves 85.52% when using our chromophore descriptors, which is 8~20% higher than those derived from other color descriptors. This demonstrates the benefit of the proposed method for automatic skin disease analysis.

  11. Efficient diagnosis of multiprocessor systems under probabilistic models

    NASA Technical Reports Server (NTRS)

    Blough, Douglas M.; Sullivan, Gregory F.; Masson, Gerald M.

    1989-01-01

    The problem of fault diagnosis in multiprocessor systems is considered under a probabilistic fault model. The focus is on minimizing the number of tests that must be conducted in order to correctly diagnose the state of every processor in the system with high probability. A diagnosis algorithm that can correctly diagnose the state of every processor with probability approaching one in a class of systems performing slightly greater than a linear number of tests is presented. A nearly matching lower bound on the number of tests required to achieve correct diagnosis in arbitrary systems is also proven. Lower and upper bounds on the number of tests required for regular systems are also presented. A class of regular systems which includes hypercubes is shown to be correctly diagnosable with high probability. In all cases, the number of tests required under this probabilistic model is shown to be significantly less than under a bounded-size fault set model. Because the number of tests that must be conducted is a measure of the diagnosis overhead, these results represent a dramatic improvement in the performance of system-level diagnosis techniques.

  12. Chip-Based Sensors for Disease Diagnosis

    NASA Astrophysics Data System (ADS)

    Fang, Zhichao

    Nucleic acid analysis is one of the most important disease diagnostic approaches in medical practice, and has been commonly used in cancer biomarker detection, bacterial speciation and many other fields in laboratory. Currently, the application of powerful research methods for genetic analysis, including the polymerase chain reaction (PCR), DNA sequencing, and gene expression profiling using fluorescence microarrays, are not widely used in hospitals and extended-care units due to high-cost, long detection times, and extensive sample preparation. Bioassays, especially chip-based electrochemical sensors, may be suitable for the next generation of rapid, sensitive, and multiplexed detection tools. Herein, we report three different microelectrode platforms with capabilities enabled by nano- and microtechnology: nanoelectrode ensembles (NEEs), nanostructured microelectrodes (NMEs), and hierarchical nanostructured microelectrodes (HNMEs), all of which are able to directly detect unpurified RNA in clinical samples without enzymatic amplification. Biomarkers that are cancer and infectious disease relevant to clinical medicine were chosen to be the targets. Markers were successfully detected with clinically-relevant sensitivity. Using peptide nucleic acids (PNAs) as probes and an electrocatalytic reporter system, NEEs were able to detect prostate cancer-related gene fusions in tumor tissue samples with 100 ng of RNA. The development of NMEs improved the sensitivity of the assay further to 10 aM of DNA target, and multiplexed detection of RNA sequences of different prostate cancer-related gene fusion types was achieved on the chip-based NMEs platform. An HNMEs chip integrated with a bacterial lysis device was able to detect as few as 25 cfu bacteria in 30 minutes and monitor the detection in real time. Bacterial detection could also be performed in neat urine samples. The development of these versatile clinical diagnostic tools could be extended to the detection of various

  13. Fractal dimension based corneal fungal infection diagnosis

    NASA Astrophysics Data System (ADS)

    Balasubramanian, Madhusudhanan; Perkins, A. Louise; Beuerman, Roger W.; Iyengar, S. Sitharama

    2006-08-01

    We present a fractal measure based pattern classification algorithm for automatic feature extraction and identification of fungus associated with an infection of the cornea of the eye. A white-light confocal microscope image of suspected fungus exhibited locally linear and branching structures. The pixel intensity variation across the width of a fungal element was gaussian. Linear features were extracted using a set of 2D directional matched gaussian-filters. Portions of fungus profiles that were not in the same focal plane appeared relatively blurred. We use gaussian filters of standard deviation slightly larger than the width of a fungus to reduce discontinuities. Cell nuclei of cornea and nerves also exhibited locally linear structure. Cell nuclei were excluded by their relatively shorter lengths. Nerves in the cornea exhibited less branching compared with the fungus. Fractal dimensions of the locally linear features were computed using a box-counting method. A set of corneal images with fungal infection was used to generate class-conditional fractal measure distributions of fungus and nerves. The a priori class-conditional densities were built using an adaptive-mixtures method to reflect the true nature of the feature distributions and improve the classification accuracy. A maximum-likelihood classifier was used to classify the linear features extracted from test corneal images as 'normal' or 'with fungal infiltrates', using the a priori fractal measure distributions. We demonstrate the algorithm on the corneal images with culture-positive fungal infiltrates. The algorithm is fully automatic and will help diagnose fungal keratitis by generating a diagnostic mask of locations of the fungal infiltrates.

  14. Self-calibrating models for dynamic monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1996-01-01

    A method for automatically building qualitative and semi-quantitative models of dynamic systems, and using them for monitoring and fault diagnosis, is developed and demonstrated. The qualitative approach and semi-quantitative method are applied to monitoring observation streams, and to design of non-linear control systems.

  15. Vibration signal models for fault diagnosis of planet bearings

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.

    2016-05-01

    Rolling element bearings are key components of planetary gearboxes. Among them, the motion of planet bearings is very complex, encompassing spinning and revolution. Therefore, planet bearing vibrations are highly intricate and their fault characteristics are completely different from those of fixed-axis case, making planet bearing fault diagnosis a difficult topic. In order to address this issue, we derive the explicit equations for calculating the characteristic frequency of outer race, rolling element and inner race fault, considering the complex motion of planet bearings. We also develop the planet bearing vibration signal model for each fault case, considering the modulation effects of load zone passing, time-varying angle between the gear pair mesh and fault induced impact force, as well as the time-varying vibration transfer path. Based on the developed signal models, we derive the explicit equations of Fourier spectrum in each fault case, and summarize the vibration spectral characteristics respectively. The theoretical derivations are illustrated by numerical simulation, and further validated experimentally and all the three fault cases (i.e. outer race, rolling element and inner race localized fault) are diagnosed.

  16. Distributed bearing fault diagnosis based on vibration analysis

    NASA Astrophysics Data System (ADS)

    Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

    2016-01-01

    Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

  17. Polycrystalline diamond based detector for Z-pinch plasma diagnosis

    SciTech Connect

    Liu Linyue; Zhao Jizhen; Chen Liang; Ouyang Xiaoping; Wang Lan

    2010-08-15

    A detector setup based on polycrystalline chemical-vapor-deposition diamond film is developed with great characteristics: low dark current (lower than 60 pA within 3 V/{mu}m), fast pulsed response time (rise time: 2-3 ns), flat spectral response (3-5 keV), easy acquisition, low cost, and relative large sensitive area. The characterizing data on Qiangguang-I accelerator show that this detector can satisfy the practical requirements in Z-pinch plasma diagnosis very well, which offers a promising prototype for the x-ray detection in Z-pinch diagnosis.

  18. Diagnosis of helicopter gearboxes using structure-based networks

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

    1995-01-01

    A connectionist network is introduced for fault diagnosis of helicopter gearboxes that incorporates knowledge of the gearbox structure and characteristics of the vibration features as its fuzzy weights. Diagnosis is performed by propagating the abnormal features of vibration measurements through this Structure-Based Connectionist Network (SBCN), the outputs of which represent the fault possibility values for individual components of the gearbox. The performance of this network is evaluated by applying it to experimental vibration data from an OH-58A helicopter gearbox. The diagnostic results indicate that the network performance is comparable to those obtained from supervised pattern classification.

  19. Breast cancer early diagnosis based on hybrid strategy.

    PubMed

    Li, Peng; Bi, Tingting; Huang, Jiuling; Li, Siben

    2014-01-01

    The frequent occurrence of breast cancer and its serious consequences have attracted worldwide attention in recent years. Problems such as low rate of accuracy and poor self-adaptability still exist in traditional diagnosis. In order to solve these problems, an AdaBoost-SVM classification algorithm, combined with the cluster boundary sampling preprocessing techniques (CBS-AdaBoost-SVM), is proposed in this paper for the early diagnosis of breast cancer. The algorithm uses machine learning method to diagnose the unknown image data. Moreover, not all of the characteristics play positive roles for classification. To address this issue the paper delete redundant features by using Rough set attribute reduction algorithm based on the genetic algorithm (GA). The effectiveness of the proposed methods are examined on DDSM by calculating its accuracy, confusion matrix, and receiver operating characteristic curves, which give important clues to the physicians for early diagnosis of breast cancer. PMID:25227050

  20. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

    PubMed Central

    Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

    2015-01-01

    Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

  1. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

    PubMed

    Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

    2015-07-06

    Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

  2. Classification models based on the level of metals in hair and nails of laryngeal cancer patients: diagnosis support or rather speculation?

    PubMed

    Golasik, Magdalena; Jawień, Wojciech; Przybyłowicz, Agnieszka; Szyfter, Witold; Herman, Małgorzata; Golusiński, Wojciech; Florek, Ewa; Piekoszewski, Wojciech

    2015-03-01

    The etiology of cancer is complex, and the disturbances in toxic and essential metals homeostasis are among many of the factors that lead to the development of malignancy. The aim of this study is to investigate the relationship between cancer risk and element status as well as cancer risk and external factors, such as diet, smoking and drinking habits, in order to support diagnosis of cancer. The samples of hair and nails obtained from patients with larynx cancer and healthy subjects were analyzed. Essential elements (Ca, Cr, Mg, Zn, Cu, Mn, and Fe), besides toxic metals (Cd, Co, and Pb), were determined using inductively coupled plasma atomic emission spectrometry (ICP-OES) and mass spectrometry (ICP-MS) techniques. The concentration of essential elements was from 1.5- (Zn) to 4.7-fold (Fe) higher in hair and from 2.4- to 3.3-fold higher in the nails of the control group compared to the patients, while the opposite trend was observed for the heavy metals. The differences between two groups in the level of metals (except for Zn) were statistically significant (p < 0.05). The association of cancer with metals and other factors was evaluated using various statistical methods, for which the best predictions were obtained using logistic regression, artificial neural networks and canonical discriminant analysis. The classifiers constructed using the data from a survey of diet and lifestyle, and analysis of elements in hair and nails, can be useful tools for estimating cancer risk and early screening of the disease.

  3. Imaging-based diagnosis of acute renal allograft rejection

    PubMed Central

    Thölking, Gerold; Schuette-Nuetgen, Katharina; Kentrup, Dominik; Pawelski, Helga; Reuter, Stefan

    2016-01-01

    Kidney transplantation is the best available treatment for patients with end stage renal disease. Despite the introduction of effective immunosuppressant drugs, episodes of acute allograft rejection still endanger graft survival. Since efficient treatment of acute rejection is available, rapid diagnosis of this reversible graft injury is essential. For diagnosis of rejection, invasive core needle biopsy of the graft is the “gold-standard”. However, biopsy carries the risk of significant graft injury and is not immediately feasible in patients taking anticoagulants. Therefore, a non-invasive tool assessing the whole organ for specific and fast detection of acute allograft rejection is desirable. We herein review current imaging-based state of the art approaches for non-invasive diagnostics of acute renal transplant rejection. We especially focus on new positron emission tomography-based as well as targeted ultrasound-based methods. PMID:27011915

  4. Broken wires diagnosis method numerical simulation based on smart cable structure

    NASA Astrophysics Data System (ADS)

    Li, Sheng; Zhou, Min; Yang, Yan

    2014-12-01

    The smart cable with embedded distributed fiber optical Bragg grating (FBG) sensors was chosen as the object to study a new diagnosis method about broken wires of the bridge cable. The diagnosis strategy based on cable force and stress distribution state of steel wires was put forward. By establishing the bridge-cable and cable-steel wires model, the broken wires sample database was simulated numerically. A method of the characterization cable state pattern which can both represent the degree and location of broken wires inside a cable was put forward. The training and predicting results of the sample database by the back propagation (BP) neural network showed that the proposed broken wires diagnosis method was feasible and expanded the broken wires diagnosis research area by using the smart cable which was used to be only representing cable force.

  5. An Intelligent Learning Diagnosis System for Web-Based Thematic Learning Platform

    ERIC Educational Resources Information Center

    Huang, Chenn-Jung; Liu, Ming-Chou; Chu, San-Shine; Cheng, Chih-Lun

    2007-01-01

    This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to…

  6. Studies in knowledge-based diagnosis of failures in robotic assembly

    NASA Technical Reports Server (NTRS)

    Lam, Raymond K.; Pollard, Nancy S.; Desai, Rajiv S.

    1990-01-01

    The telerobot diagnostic system (TDS) is a knowledge-based system that is being developed for identification and diagnosis of failures in the space robotic domain. The system is able to isolate the symptoms of the failure, generate failure hypotheses based on these symptoms, and test their validity at various levels by interpreting or simulating the effects of the hypotheses on results of plan execution. The implementation of the TDS is outlined. The classification of failures and the types of system models used by the TDS are discussed. A detailed example of the TDS approach to failure diagnosis is provided.

  7. A Cough-Based Algorithm for Automatic Diagnosis of Pertussis

    PubMed Central

    Pramono, Renard Xaviero Adhi; Imtiaz, Syed Anas; Rodriguez-Villegas, Esther

    2016-01-01

    Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. PMID:27583523

  8. A Cough-Based Algorithm for Automatic Diagnosis of Pertussis.

    PubMed

    Pramono, Renard Xaviero Adhi; Imtiaz, Syed Anas; Rodriguez-Villegas, Esther

    2016-01-01

    Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. PMID:27583523

  9. Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

    ERIC Educational Resources Information Center

    Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

    2009-01-01

    This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

  10. 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.

  11. 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

  12. A Fault Diagnosis Approach for Rolling Bearings Based on EMD Method and Eigenvector Algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Jinyu; Huang, Xianxiang

    Fault diagnosis of rolling bearings is still a very important and difficult research task on engineering. After analyzing the shortcomings of current bearing fault diagnosis technologies, a new approach based on Empirical Mode Decomposition (EMD) and blind equalization eigenvector algorithm (EVA) for rolling bearings fault diagnosis is proposed. In this approach, the characteristic high-frequency signal with amplitude and channel modulation of a rolling bearing with local damage is first separated from the mechanical vibration signal as an Intrinsic Mode Function (IMF) by using EMD, then the source impact vibration signal yielded by local damage is extracted by means of a EVA model and algorithm. Finally, the presented approach is used to analyze an impacting experiment and two real signals collected from rolling bearings with outer race damage or inner race damage. The results show that the EMD and EVA based approach can effectively detect rolling bearing fault.

  13. Tropical cyclogenesis: a numerical diagnosis based on helical flow organization

    NASA Astrophysics Data System (ADS)

    Levina, G. V.; Montgomery, M. T.

    2014-10-01

    A numerical diagnosis for tropical cyclogenesis is proposed based on nearcloud-resolving atmospheric simulation. Calculation and analyses of helical and energetic characteristics together with hydro- and thermodynamic flow fields allow the diagnosis of tropical cyclogenesis as an event when the primary and secondary circulations become linked on system scales thereby making the nascent large-scale vortex helical. A key process of vertical vorticity generation from horizontal components and its amplification by special convective coherent structures - Vortical Hot Towers (VHTs) - is highlighted. The process is found to be a pathway for generation of a velocity field with linked vortex lines of horizontal and vertical vorticity on local and system scales. The role of VHTs as connectors of the primary and secondary circulation is emphasized.

  14. PACS-Based Computer-Aided Detection and Diagnosis

    NASA Astrophysics Data System (ADS)

    Huang, H. K. (Bernie); Liu, Brent J.; Le, Anh HongTu; Documet, Jorge

    The ultimate goal of Picture Archiving and Communication System (PACS)-based Computer-Aided Detection and Diagnosis (CAD) is to integrate CAD results into daily clinical practice so that it becomes a second reader to aid the radiologist's diagnosis. Integration of CAD and Hospital Information System (HIS), Radiology Information System (RIS) or PACS requires certain basic ingredients from Health Level 7 (HL7) standard for textual data, Digital Imaging and Communications in Medicine (DICOM) standard for images, and Integrating the Healthcare Enterprise (IHE) workflow profiles in order to comply with the Health Insurance Portability and Accountability Act (HIPAA) requirements to be a healthcare information system. Among the DICOM standards and IHE workflow profiles, DICOM Structured Reporting (DICOM-SR); and IHE Key Image Note (KIN), Simple Image and Numeric Report (SINR) and Post-processing Work Flow (PWF) are utilized in CAD-HIS/RIS/PACS integration. These topics with examples are presented in this chapter.

  15. BCC skin cancer diagnosis based on texture analysis techniques

    NASA Astrophysics Data System (ADS)

    Chuang, Shao-Hui; Sun, Xiaoyan; Chang, Wen-Yu; Chen, Gwo-Shing; Huang, Adam; Li, Jiang; McKenzie, Frederic D.

    2011-03-01

    In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% and 95%, respectively.

  16. Mutation-based prenatal diagnosis of Herlitz junctional epidermolysis bullosa.

    PubMed

    Christiano, A M; Pulkkinen, L; McGrath, J A; Uitto, J

    1997-04-01

    Epidermolysis bullosa (EB) is a group of heritable diseases which manifest with blistering and erosions of the skin and mucous membranes. Due of life-threatening complications and significant long-term morbidity associated with the severe, neonatal lethal (Herlitz) form of junctional EB (H-JEB), there has been a demand for prenatal diagnosis from families at risk for recurrence. Previously, the only reliable method of prenatal diagnosis of EB was a fetal skin biopsy performed at 16-20 weeks' gestation and analysed by electron microscopy. Recently, the genes LAMA3, LAMB3, and LAMC2, encoding the polypeptide subunits of laminin 5, an anchoring filament protein, have been shown to contain mutations in H-JEB. In this study, direct detection of pathogenetic mutations in the laminin 5 genes was used to perform polymerase chain reaction (PCR)-based prenatal testing. DNA was obtained by chorionic villus sampling (CVS) at 10-15 weeks or amniocentesis at 12-19 weeks' gestation in 15 families at risk for recurrence of JEB. In 13 cases, the fetus was predicted to be either genetically normal or a clinically unaffected carrier of a mutation in one allele. These predictions have been validated in all cases by the birth of a healthy child. In two cases, an affected fetus was predicted, and the diagnosis was confirmed by subsequent fetal skin biopsy. These results demonstrate that DNA-based prenatal testing offers an early, expedient, and accurate method of prenatal diagnosis or an exclusion of Herlitz JEB. PMID:9160387

  17. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

    PubMed

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

  18. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

    PubMed Central

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

  19. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

    PubMed

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

  20. Self-calibrating models for dynamic monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1994-01-01

    The present goal in qualitative reasoning is to develop methods for automatically building qualitative and semiquantitative models of dynamic systems and to use them for monitoring and fault diagnosis. The qualitative approach to modeling provides a guarantee of coverage while our semiquantitative methods support convergence toward a numerical model as observations are accumulated. We have developed and applied methods for automatic creation of qualitative models, developed two methods for obtaining tractable results on problems that were previously intractable for qualitative simulation, and developed more powerful methods for learning semiquantitative models from observations and deriving semiquantitative predictions from them. With these advances, qualitative reasoning comes significantly closer to realizing its aims as a practical engineering method.

  1. Diagnosis and office-based treatment of urinary incontinence in adults. Part one: diagnosis and testing

    PubMed Central

    Heidelbaugh, Joel J.; Jimbo, Masahito

    2013-01-01

    Urinary incontinence is a common problem in both men and women. This review article addresses its prevalence, risk factors, cost, the various types of incontinence, as well as how to diagnose them. The US Preventive Services Task Force, the Cochrane Database of Systematic Reviews, and PubMed were reviewed for articles focusing on urinary incontinence. Incontinence is a common problem with a high societal cost. It is frequently underreported by patients so it is appropriate for primary-care providers to screen all women and older men during visits. A thorough history and physical examination combined with easy office-based tests can often yield a clear diagnosis and rule out other transient illnesses contributing to the incontinence. Specialist referral is occasionally needed in specific situations before embarking on a treatment plan. PMID:23904857

  2. Autoregressive modelling for rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Al-Bugharbee, H.; Trendafilova, I.

    2015-07-01

    In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.

  3. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    PubMed

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing.

  4. Knowledge based acquisition of rules for medical diagnosis

    SciTech Connect

    Drastal, G.A.; Kulikowski, C.A.

    1982-01-01

    Medical consultation systems in the expert framework contain rules written under the guidance of expert physicians. The authors present a methodology and preliminary implementation of a system which learns compiled rule chains from positive case examples of a diagnostic class and negative examples of alternative diagnostic classes. Rule acquisition is guided by the constraints of physiological process models represented in the system. Evaluation of the system is proceeding in the area of glaucoma diagnosis, and an example of an experiment in this domain is included. 9 references.

  5. A novel diagnosis method for a Hall plates-based rotary encoder with a magnetic concentrator.

    PubMed

    Meng, Bumin; Wang, Yaonan; Sun, Wei; Yuan, Xiaofang

    2014-01-01

    In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly. PMID:25090417

  6. A diagnosis system using object-oriented fault tree models

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; Patterson-Hine, F. A.

    1990-01-01

    Spaceborne computing systems must provide reliable, continuous operation for extended periods. Due to weight, power, and volume constraints, these systems must manage resources very effectively. A fault diagnosis algorithm is described which enables fast and flexible diagnoses in the dynamic distributed computing environments planned for future space missions. The algorithm uses a knowledge base that is easily changed and updated to reflect current system status. Augmented fault trees represented in an object-oriented form provide deep system knowledge that is easy to access and revise as a system changes. Given such a fault tree, a set of failure events that have occurred, and a set of failure events that have not occurred, this diagnosis system uses forward and backward chaining to propagate causal and temporal information about other failure events in the system being diagnosed. Once the system has established temporal and causal constraints, it reasons backward from heuristically selected failure events to find a set of basic failure events which are a likely cause of the occurrence of the top failure event in the fault tree. The diagnosis system has been implemented in common LISP using Flavors.

  7. Tumors of the cranial base: Diagnosis and treatment

    SciTech Connect

    Sekhar, L.N.; Schramm, V.L.

    1987-01-01

    The first section of this book highlights the differences and similarities in the pathology and biology of the various types of neoplasms of the cranial base. The second section covers improvements in radiological diagnosis with the advent of computed tomography, magnetic resonance imaging and a better knowledge of radiological anatomy. It also examines the significance and proper evaluation of minor symptoms to enable earlier diagnosis, as well as the advances in interventional radiology that have produced the balloon occlusion text and tumor embolization. Section three is on advanced neuroanesthetic techniques and intraoperative neurophysiological monitoring. Section four describes specialized treatment modalities including microsurgical resection with the laser, radiation therapy and chemotherapy. Section five reviews the latest techniques for reconstruction of the cranial base following resection, as well as the preservation and reconstruction of cranial nerves and cerebral blood vessels exposed during the surgery. The final three sections examine the lesions and surgical techniques specific to the different anatomical regions, i.e, the anterior, middle and posterior cranial base.

  8. Cancer Mortality in People Treated with Antidepressants before Cancer Diagnosis: A Population Based Cohort Study

    PubMed Central

    Sun, Yuelian; Vedsted, Peter; Fenger-Grøn, Morten; Wu, Chun Sen; Bech, Bodil Hammer; Olsen, Jørn; Benros, Michael Eriksen; Vestergaard, Mogens

    2015-01-01

    Background Depression is common after a cancer diagnosis and is associated with an increased mortality, but it is unclear whether depression occurring before the cancer diagnosis affects cancer mortality. We aimed to study cancer mortality of people treated with antidepressants before cancer diagnosis. Methods and Findings We conducted a population based cohort study of all adults diagnosed with cancer between January 2003 and December 2010 in Denmark (N = 201,662). We obtained information on cancer from the Danish Cancer Registry, on the day of death from the Danish Civil Registry, and on redeemed antidepressants from the Danish National Prescription Registry. Current users of antidepressants were defined as those who redeemed the latest prescription of antidepressant 0–4 months before cancer diagnosis (irrespective of earlier prescriptions), and former users as those who redeemed the latest prescription five or more months before cancer diagnosis. We estimated an all-cause one-year mortality rate ratio (MRR) and a conditional five-year MRR for patients who survived the first year after cancer diagnosis and confidence interval (CI) using a Cox proportional hazards regression model. Overall, 33,111 (16.4%) patients redeemed at least one antidepressant prescription in the three years before cancer diagnosis of whom 21,851 (10.8%) were current users at the time of cancer diagnosis. Current antidepressant users had a 32% higher one-year mortality (MRR = 1.32, 95% CI: 1.29–1.35) and a 22% higher conditional five-year mortality (MRR = 1.22, 95% CI: 1.17–1.26) if patients survived the first year after the cancer diagnosis than patients not redeeming antidepressants. The one-year mortality was particularly high for patients who initiated antidepressant treatment within four months before cancer diagnosis (MRR = 1.54, 95% CI: 1.47–1.61). Former users had no increased cancer mortality. Conclusions Initiation of antidepressive treatment prior to cancer diagnosis is

  9. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    PubMed Central

    Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  10. Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

    PubMed

    Yin, Hong; Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

  11. An expert fitness diagnosis system based on elastic cloud computing.

    PubMed

    Tseng, Kevin C; Wu, Chia-Chuan

    2014-01-01

    This paper presents an expert diagnosis system based on cloud computing. It classifies a user's fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user's physiological data, such as age, gender, and body mass index (BMI). In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.

  12. An Expert Fitness Diagnosis System Based on Elastic Cloud Computing

    PubMed Central

    Tseng, Kevin C.; Wu, Chia-Chuan

    2014-01-01

    This paper presents an expert diagnosis system based on cloud computing. It classifies a user's fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user's physiological data, such as age, gender, and body mass index (BMI). In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service. PMID:24723842

  13. Adaptive PCA based fault diagnosis scheme in imperial smelting process.

    PubMed

    Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

    2014-09-01

    In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently.

  14. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    SciTech Connect

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; Sheng, Shuangwen

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issue is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.

  15. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    DOE PAGESBeta

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; Sheng, Shuangwen

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

  16. Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Mengshoel, Ole

    2008-01-01

    Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

  17. Polymer electrolyte membrane fuel cell fault diagnosis based on empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Damour, Cédric; Benne, Michel; Grondin-Perez, Brigitte; Bessafi, Miloud; Hissel, Daniel; Chabriat, Jean-Pierre

    2015-12-01

    Diagnosis tool for water management is relevant to improve the reliability and lifetime of polymer electrolyte membrane fuel cells (PEMFCs). This paper presents a novel signal-based diagnosis approach, based on Empirical Mode Decomposition (EMD), dedicated to PEMFCs. EMD is an empirical, intuitive, direct and adaptive signal processing method, without pre-determined basis functions. The proposed diagnosis approach relies on the decomposition of FC output voltage to detect and isolate flooding and drying faults. The low computational cost of EMD, the reduced number of required measurements, and the high diagnosis accuracy of flooding and drying faults diagnosis make this approach a promising online diagnosis tool for PEMFC degraded modes management.

  18. NGS-based Molecular diagnosis of 105 eyeGENE(®) probands with Retinitis Pigmentosa.

    PubMed

    Ge, Zhongqi; Bowles, Kristen; Goetz, Kerry; Scholl, Hendrik P N; Wang, Feng; Wang, Xinjing; Xu, Shan; Wang, Keqing; Wang, Hui; Chen, Rui

    2015-12-15

    The National Ophthalmic Disease Genotyping and Phenotyping Network (eyeGENE(®)) was established in an effort to facilitate basic and clinical research of human inherited eye disease. In order to provide high quality genetic testing to eyeGENE(®)'s enrolled patients which potentially aids clinical diagnosis and disease treatment, we carried out a pilot study and performed Next-generation sequencing (NGS) based molecular diagnosis for 105 Retinitis Pigmentosa (RP) patients randomly selected from the network. A custom capture panel was designed, which incorporated 195 known retinal disease genes, including 61 known RP genes. As a result, disease-causing mutations were identified in 52 out of 105 probands (solving rate of 49.5%). A total of 82 mutations were identified, and 48 of them were novel. Interestingly, for three probands the molecular diagnosis was inconsistent with the initial clinical diagnosis, while for five probands the molecular information suggested a different inheritance model other than that assigned by the physician. In conclusion, our study demonstrated that NGS target sequencing is efficient and sufficiently precise for molecular diagnosis of a highly heterogeneous patient cohort from eyeGENE(®).

  19. Microfluidic chip-based technologies: emerging platforms for cancer diagnosis

    PubMed Central

    2013-01-01

    The development of early and personalized diagnostic protocols is considered the most promising avenue to decrease mortality from cancer and improve outcome. The emerging microfluidic-based analyzing platforms hold high promises to fulfill high-throughput and high-precision screening with reduced equipment cost and low analysis time, as compared to traditional bulky counterparts in bench-top laboratories. This article overviewed the potential applications of microfluidic technologies for detection and monitoring of cancer through nucleic acid and protein biomarker analysis. The implications of the technologies in cancer cytology that can provide functional personalized diagnosis were highlighted. Finally, the future niches for using microfluidic-based systems in tumor screening were briefly discussed. PMID:24070124

  20. Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

    NASA Astrophysics Data System (ADS)

    Zhang, Han

    Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

  1. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

    PubMed Central

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information. PMID:25938760

  2. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    PubMed

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

  3. Wavelet-Based Real-Time Diagnosis of Complex Systems

    NASA Technical Reports Server (NTRS)

    Gulati, Sandeep; Mackey, Ryan

    2003-01-01

    A new method of robust, autonomous real-time diagnosis of a time-varying complex system (e.g., a spacecraft, an advanced aircraft, or a process-control system) is presented here. It is based upon the characterization and comparison of (1) the execution of software, as reported by discrete data, and (2) data from sensors that monitor the physical state of the system, such as performance sensors or similar quantitative time-varying measurements. By taking account of the relationship between execution of, and the responses to, software commands, this method satisfies a key requirement for robust autonomous diagnosis, namely, ensuring that control is maintained and followed. Such monitoring of control software requires that estimates of the state of the system, as represented within the control software itself, are representative of the physical behavior of the system. In this method, data from sensors and discrete command data are analyzed simultaneously and compared to determine their correlation. If the sensed physical state of the system differs from the software estimate (see figure) or if the system fails to perform a transition as commanded by software, or such a transition occurs without the associated command, the system has experienced a control fault. This method provides a means of detecting such divergent behavior and automatically generating an appropriate warning.

  4. Compound fault diagnosis of gearboxes based on GFT component extraction

    NASA Astrophysics Data System (ADS)

    Ou, Lu; Yu, Dejie

    2016-11-01

    Compound fault diagnosis of gearboxes is of great importance to the long-term safe operation of rotating machines, and the key is to separate different fault components. In this paper, the path graph is introduced into the vibration signal analysis and the graph Fourier transform (GFT) of vibration signals are investigated from the graph spectrum domain. To better extract the fault components in gearboxes, a new adjacency weight matrix is defined and then the GFT of simulation signals of the gear and the bearing with localized faults are analyzed. Further, since the GFT graph spectrum of the gear fault component and the bearing fault component are mainly distributed in the low-order region and the high-order region, respectively, a novel method for the compound fault diagnosis of gearboxes based on GFT component extraction is proposed. In this method, the nonzero ratios, which are introduced to analyze the eigenvectors auxiliary, and the GFT of a gearbox vibration signal, are firstly calculated. Then, the order thresholds for reconstructed fault components are determined and the fault components are extracted. Finally, the Hilbert demodulation analyses are conducted. According to the envelope spectra of the fault components, the faults of the gear and the bearing can be diagnosed respectively. The performance of the proposed method is validated by the simulation data and the experiment signals from a gearbox with compound faults.

  5. A Clinical Model for the Diagnosis and Management of Patients with Cervical Spine Syndromes

    PubMed Central

    Murphy, Donald R.

    2004-01-01

    Background: Disorders of the cervical spine are common and often disabling. The etiology of these disorders is often multifactorial and a comprehensive approach to both diagnosis and management is essential to successful resolution. Objective: This article provides an overview of a clinical model of the diagnosis and management of patients with disorders related to the cervical spine. This model is based in part on the scientific literature, clinical experience, and communication with other practitioners over the course of the past 20 years. Discussion: The clinical model presented here involves taking a systematic approach to diagnosis, and management. The diagnostic process is one that asks three essential questions. The answers to these questions then guides the management process, allowing the physician to apply specific methods that address the many factors that can be involved in each individual patient. This clinical model allows the physician to individualize the management strategy while utilizing principles that can be applied to all patients. At times, the management strategy must be multidisciplinary, and cooperation with other physicians and therapists is often necessary for effective patient care. This model is currently being used by the author in practice, as well as forming the basis upon which further research can be conducted to refine or, if necessary, abandon any of its aspects, as the evidence dictates. It is the purpose of this paper to present this clinical model and the clinical and scientific evidence, or lack thereof, of its components. PMID:17987214

  6. Diagnosis of Parkinsonian disorders using a channelized Hotelling observer model: Proof of principle

    SciTech Connect

    Bal, H.; Bal, G.; Acton, P. D.

    2007-10-15

    Imaging dopamine transporters using PET and SPECT probes is a powerful technique for the early diagnosis of Parkinsonian disorders. In order to perform automated accurate diagnosis of these diseases, a channelized Hotelling observer (CHO) based model was developed and evaluated using the SPECT tracer [Tc-99m]TRODAT-1. Computer simulations were performed using a digitized striatal phantom to characterize early stages of the disease (20 lesion-present cases with varying lesion size and contrast). Projection data, modeling the effects of attenuation and geometric response function, were obtained for each case. Statistical noise levels corresponding to those observed clinically were added to the projection data to obtain 100 noise realizations for each case. All the projection data were reconstructed, and a subset of the transaxial slices containing the striatum was summed and used for further analysis. CHO models, using the Laguerre-Gaussian functions as channels, were designed for two cases: (1) By training the model using individual lesion-present samples and (2) by training the model using pooled lesion-present samples. A decision threshold obtained for each CHO model was used to classify the study population (n=40). It was observed that individual lesion trained CHO models gave high diagnostic accuracy for lesions that were larger than those used to train the model and vice-versa. On the other hand, the pooled CHO model was found to give a high diagnostic accuracy for all the lesion cases (average diagnostic accuracy=0.95{+-}0.07; p<0.0001 Fisher's exact test). Based on our results, we conclude that a CHO model has the potential to provide early and accurate diagnosis of Parkinsonian disorders, thereby improving patient management.

  7. A diagnosis-based clinical decision rule for spinal pain part 2: review of the literature

    PubMed Central

    Murphy, Donald R; Hurwitz, Eric L; Nelson, Craig F

    2008-01-01

    Background Spinal pain is a common and often disabling problem. The research on various treatments for spinal pain has, for the most part, suggested that while several interventions have demonstrated mild to moderate short-term benefit, no single treatment has a major impact on either pain or disability. There is great need for more accurate diagnosis in patients with spinal pain. In a previous paper, the theoretical model of a diagnosis-based clinical decision rule was presented. The approach is designed to provide the clinician with a strategy for arriving at a specific working diagnosis from which treatment decisions can be made. It is based on three questions of diagnosis. In the current paper, the literature on the reliability and validity of the assessment procedures that are included in the diagnosis-based clinical decision rule is presented. Methods The databases of Medline, Cinahl, Embase and MANTIS were searched for studies that evaluated the reliability and validity of clinic-based diagnostic procedures for patients with spinal pain that have relevance for questions 2 (which investigates characteristics of the pain source) and 3 (which investigates perpetuating factors of the pain experience). In addition, the reference list of identified papers and authors' libraries were searched. Results A total of 1769 articles were retrieved, of which 138 were deemed relevant. Fifty-one studies related to reliability and 76 related to validity. One study evaluated both reliability and validity. Conclusion Regarding some aspects of the DBCDR, there are a number of studies that allow the clinician to have a reasonable degree of confidence in his or her findings. This is particularly true for centralization signs, neurodynamic signs and psychological perpetuating factors. There are other aspects of the DBCDR in which a lesser degree of confidence is warranted, and in which further research is needed. PMID:18694490

  8. Diagnosis of Dengue Infection Using Conventional and Biosensor Based Techniques.

    PubMed

    Parkash, Om; Shueb, Rafidah Hanim

    2015-10-19

    Dengue is an arthropod-borne viral disease caused by four antigenically different serotypes of dengue virus. This disease is considered as a major public health concern around the world. Currently, there is no licensed vaccine or antiviral drug available for the prevention and treatment of dengue disease. Moreover, clinical features of dengue are indistinguishable from other infectious diseases such as malaria, chikungunya, rickettsia and leptospira. Therefore, prompt and accurate laboratory diagnostic test is urgently required for disease confirmation and patient triage. The traditional diagnostic techniques for the dengue virus are viral detection in cell culture, serological testing, and RNA amplification using reverse transcriptase PCR. This paper discusses the conventional laboratory methods used for the diagnosis of dengue during the acute and convalescent phase and highlights the advantages and limitations of these routine laboratory tests. Subsequently, the biosensor based assays developed using various transducers for the detection of dengue are also reviewed.

  9. Computer-based assessment for facioscapulohumeral dystrophy diagnosis.

    PubMed

    Chambers, O; Milenković, J; Pražnikar, A; Tasič, J F

    2015-06-01

    The paper presents a computer-based assessment for facioscapulohumeral dystrophy (FSHD) diagnosis through characterisation of the fat and oedema percentages in the muscle region. A novel multi-slice method for the muscle-region segmentation in the T1-weighted magnetic resonance images is proposed using principles of the live-wire technique to find the path representing the muscle-region border. For this purpose, an exponential cost function is used that incorporates the edge information obtained after applying the edge-enhancement algorithm formerly designed for the fingerprint enhancement. The difference between the automatic segmentation and manual segmentation performed by a medical specialists is characterised using the Zijdenbos similarity index, indicating a high accuracy of the proposed method. Finally, the fat and oedema are quantified from the muscle region in the T1-weighted and T2-STIR magnetic resonance images, respectively, using the fuzzy c-mean clustering approach for 10 FSHD patients. PMID:25910520

  10. Diagnosis of Dengue Infection Using Conventional and Biosensor Based Techniques

    PubMed Central

    Parkash, Om; Hanim Shueb, Rafidah

    2015-01-01

    Dengue is an arthropod-borne viral disease caused by four antigenically different serotypes of dengue virus. This disease is considered as a major public health concern around the world. Currently, there is no licensed vaccine or antiviral drug available for the prevention and treatment of dengue disease. Moreover, clinical features of dengue are indistinguishable from other infectious diseases such as malaria, chikungunya, rickettsia and leptospira. Therefore, prompt and accurate laboratory diagnostic test is urgently required for disease confirmation and patient triage. The traditional diagnostic techniques for the dengue virus are viral detection in cell culture, serological testing, and RNA amplification using reverse transcriptase PCR. This paper discusses the conventional laboratory methods used for the diagnosis of dengue during the acute and convalescent phase and highlights the advantages and limitations of these routine laboratory tests. Subsequently, the biosensor based assays developed using various transducers for the detection of dengue are also reviewed. PMID:26492265

  11. A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer

    PubMed Central

    Si, Hongzong; Liu, Shihai; Li, Xianchao; Gao, Caihong; Cui, Lianhua; Li, Chuan; Yang, Xue; Yao, Xiaojun

    2015-01-01

    Background Lung cancer is an important and common cancer that constitutes a major public health problem, but early detection of small cell lung cancer can significantly improve the survival rate of cancer patients. A number of serum biomarkers have been used in the diagnosis of lung cancers; however, they exhibit low sensitivity and specificity. Methods We used biochemical methods to measure blood levels of lactate dehydrogenase (LDH), C-reactive protein (CRP), Na+, Cl-, carcino-embryonic antigen (CEA), and neuron specific enolase (NSE) in 145 small cell lung cancer (SCLC) patients and 155 non-small cell lung cancer and 155 normal controls. A gene expression programming (GEP) model and Receiver Operating Characteristic (ROC) curves incorporating these biomarkers was developed for the auxiliary diagnosis of SCLC. Results After appropriate modification of the parameters, the GEP model was initially set up based on a training set of 115 SCLC patients and 125 normal controls for GEP model generation. Then the GEP was applied to the remaining 60 subjects (the test set) for model validation. GEP successfully discriminated 281 out of 300 cases, showing a correct classification rate for lung cancer patients of 93.75% (225/240) and 93.33% (56/60) for the training and test sets, respectively. Another GEP model incorporating four biomarkers, including CEA, NSE, LDH, and CRP, exhibited slightly lower detection sensitivity than the GEP model, including six biomarkers. We repeat the models on artificial neural network (ANN), and our results showed that the accuracy of GEP models were higher than that in ANN. GEP model incorporating six serum biomarkers performed by NSCLC patients and normal controls showed low accuracy than SCLC patients and was enough to prove that the GEP model is suitable for the SCLC patients. Conclusion We have developed a GEP model with high sensitivity and specificity for the auxiliary diagnosis of SCLC. This GEP model has the potential for the wide use

  12. Robustness of Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Templin, Jonathan L.; Henson, Robert A.; Templin, Sara E.; Roussos, Louis

    2008-01-01

    Several types of parameterizations of attribute correlations in cognitive diagnosis models use the reduced reparameterized unified model. The general approach presumes an unconstrained correlation matrix with K(K - 1)/2 parameters, whereas the higher order approach postulates K parameters, imposing a unidimensional structure on the correlation…

  13. Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes.

    PubMed

    Liu, Qiang; Chai, Tianyou; Wang, Hong; Qin, Si-Zhao Joe

    2011-12-01

    The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of faults. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and fault diagnosis method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension fault diagnosis method is designed using the estimated tensions. Finally, the proposed tension estimation and fault diagnosis method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method.

  14. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    NASA Technical Reports Server (NTRS)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1996-01-01

    Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

  15. Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Jiang, Huiming; Chen, Jin; Dong, Guangming; Liu, Tao; Chen, Gang

    2015-02-01

    Based on the traditional theory of singular value decomposition (SVD), singular values (SVs) and ratios of neighboring singular values (NSVRs) are introduced to the feature extraction of vibration signals. The proposed feature extraction method is called SV-NSVR. Combined with selected SV-NSVR features, continuous hidden Markov model (CHMM) is used to realize the automatic classification. Then the SV-NSVR and CHMM based method is applied in fault diagnosis and performance assessment of rolling element bearings. The simulation and experimental results show that this method has a higher accuracy for the bearing fault diagnosis compared with those using other SVD features, and it is effective for the performance assessment of rolling element bearings.

  16. Computer-Aided Diagnosis and Quantification of Cirrhotic Livers Based on Morphological Analysis and Machine Learning

    PubMed Central

    Chen, Yen-Wei; Luo, Jie; Dong, Chunhua; Han, Xianhua; Tateyama, Tomoko; Furukawa, Akira; Kanasaki, Shuzo

    2013-01-01

    It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we proposed a morphological analysis method based on statistical shape models (SSMs) of the liver and spleen for computer-aided diagnosis and quantification of the chronic liver. We constructed not only the liver SSM but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. The effective modes are selected based on both its accumulation contribution rate and its correlation with doctor's opinions (stage labels). We then learn a mapping function between the selected mode and the stage of chronic liver. The mapping function was used for diagnosis and staging of chronic liver diseases. PMID:24187579

  17. Applied Neuroimaging to the Diagnosis of Alzheimer's Disease: A Multicriteria Model

    NASA Astrophysics Data System (ADS)

    Tamanini, Isabelle; de Castro, Ana Karoline; Pinheiro, Plácido Rogério; Pinheiro, Mirian Calíope Dantas

    In the last few years, Alzheimer's disease has been the most frequent cause of dementia and it is responsible, alone or in association with other diseases, for 50% of the cases in western countries. Dementias are syndromes characterized by a decline in memory and other neuropsychological changes, especially occurring in elderly people and increasing exponentially along the aging process. The main focus of this work is to develop a multicriteria model for aiding in decision making on the diagnosis of Alzheimer's disease by using the Aranau Tool, structured on the Verbal Decision Analysis. In this work, the modeling and evaluation processes were conducted with the aid of a medical expert, bibliographic sources and questionnaires. The questionnaires taken into account were based mainly on patients' neuroimaging tests, and we analyzed wheter or not there were problems in the patients' brain that could be relevant to the diagnosis of Alzheimer's disease.

  18. The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator.

    PubMed

    Roh, S D; Kim, S W; Cho, W S

    2001-10-01

    The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control.

  19. Structural modal parameter identification and damage diagnosis based on Hilbert-Huang transform

    NASA Astrophysics Data System (ADS)

    Han, Jianping; Zheng, Peijuan; Wang, Hongtao

    2014-03-01

    Traditional modal parameter identification methods have many disadvantages, especially when used for processing nonlinear and non-stationary signals. In addition, they are usually not able to accurately identify the damping ratio and damage. In this study, methods based on the Hilbert-Huang transform (HHT) are investigated for structural modal parameter identification and damage diagnosis. First, mirror extension and prediction via a radial basis function (RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition (EMD), which is a crucial part of HHT. Then, the approaches based on HHT combined with other techniques, such as the random decrement technique (RDT), natural excitation technique (NExT) and stochastic subspace identification (SSI), are proposed to identify modal parameters of structures. Furthermore, a damage diagnosis method based on the HHT is also proposed. Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure. The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure. Finally, acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches. The results show that the proposed approaches based on HHT for modal parameter identification and damage diagnosis are reliable and practical.

  20. Are diagnosis specific outcome indicators based on administrative data useful in assessing quality of hospital care?

    PubMed Central

    Scott, I; Youlden, D; Coory, M

    2004-01-01

    Background: Hospital performance reports based on administrative data should distinguish differences in quality of care between hospitals from case mix related variation and random error effects. A study was undertaken to determine which of 12 diagnosis-outcome indicators measured across all hospitals in one state had significant risk adjusted systematic (or special cause) variation (SV) suggesting differences in quality of care. For those that did, we determined whether SV persists within hospital peer groups, whether indicator results correlate at the individual hospital level, and how many adverse outcomes would be avoided if all hospitals achieved indicator values equal to the best performing 20% of hospitals. Methods: All patients admitted during a 12 month period to 180 acute care hospitals in Queensland, Australia with heart failure (n = 5745), acute myocardial infarction (AMI) (n = 3427), or stroke (n = 2955) were entered into the study. Outcomes comprised in-hospital deaths, long hospital stays, and 30 day readmissions. Regression models produced standardised, risk adjusted diagnosis specific outcome event ratios for each hospital. Systematic and random variation in ratio distributions for each indicator were then apportioned using hierarchical statistical models. Results: Only five of 12 (42%) diagnosis-outcome indicators showed significant SV across all hospitals (long stays and same diagnosis readmissions for heart failure; in-hospital deaths and same diagnosis readmissions for AMI; and in-hospital deaths for stroke). Significant SV was only seen for two indicators within hospital peer groups (same diagnosis readmissions for heart failure in tertiary hospitals and inhospital mortality for AMI in community hospitals). Only two pairs of indicators showed significant correlation. If all hospitals emulated the best performers, at least 20% of AMI and stroke deaths, heart failure long stays, and heart failure and AMI readmissions could be avoided

  1. Modeling power systems for diagnosis - How good is good enough?

    NASA Astrophysics Data System (ADS)

    Fesq, Lorraine M.; Stephan, Amy; McNamee, Lawrence

    Space power system automation requires comprehensive fault detection, isolation and recovery. Extending current approaches to on-board power management to isolate and resolve (as well as detect) failures would greatly increase the size and complexity of the flight software. Instead, we identified a technique called model-based constraint suspension which can isolate a large percentage of failures, including unforeseen failures, with minimal increases in code size. Adapting this digital technique to an electrical power system required us to address several key issues including model construction and fidelity. This paper contrasts several approaches to model-based diagnostics to our diagnostic system, Marple, which has been demonstrated on a power testbed.

  2. Application of a diagnosis-based clinical decision guide in patients with low back pain

    PubMed Central

    2011-01-01

    Background Low back pain (LBP) is common and costly. Development of accurate and efficacious methods of diagnosis and treatment has been identified as a research priority. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based means to apply the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to present the prevalence of findings using the DBCDG in consecutive patients with LBP. Methods Demographic, diagnostic and baseline outcome measure data were gathered on a cohort of LBP patients examined by one of three examiners trained in the application of the DBCDG. Results Data were gathered on 264 patients. Signs of visceral disease or potentially serious illness were found in 2.7%. Centralization signs were found in 41%, lumbar and sacroiliac segmental signs in 23% and 27%, respectively and radicular signs were found in 24%. Clinically relevant myofascial signs were diagnosed in 10%. Dynamic instability was diagnosed in 63%, fear beliefs in 40%, central pain hypersensitivity in 5%, passive coping in 3% and depression in 3%. Conclusion The DBCDG can be applied in a busy private practice environment. Further studies are needed to investigate clinically relevant means to identify central pain hypersensitivity, poor coping and depression, correlations and patterns among the diagnostic components of the DBCDG as well as inter-examiner reliability and efficacy of treatment based on the DBCDG. PMID:22018026

  3. Photoacoustic-based nanomedicine for cancer diagnosis and therapy.

    PubMed

    Sim, Changbeom; Kim, Haemin; Moon, Hyungwon; Lee, Hohyeon; Chang, Jin Ho; Kim, Hyuncheol

    2015-04-10

    Photoacoustic imaging is the latest promising diagnostic modality that has various advantages such as high spatial resolution, deep penetration depth, and use of non-ionizing radiation. It also employs a non-invasive imaging technique and optically functionalized imaging. The goal of this study was to develop a nanomedicine for simultaneous cancer therapy and diagnosis based on photoacoustic imaging. Human serum albumin nanoparticles loaded with melanin and paclitaxel (HMP-NPs) were developed using the desolvation technique. The photoacoustic-based diagnostic and chemotherapeutic properties of HMP-NPs were evaluated through in vitro and in vivo experiments. The size and zeta potential of the HMP-NPs were found to be 192.8±21.11nm and -22.2±4.39mV, respectively. In in vitro experiments, HMP-NPs produced increased photoacoustic signal intensity because of the loaded melanin and decreased cellular viability because of the encapsulated paclitaxel, compared to the free human serum albumin nanoparticles (the control). In vivo experiments showed that the HMP-NPs efficiently accumulated inside the tumor, resulting in the enhanced photoacoustic signal intensity in the tumor site, compared to the normal tissues. The in vivo chemotherapy study demonstrated that HMP-NPs had the capability to treat cancer for an extended period. In conclusion, HMP-NPs were simultaneously capable of photoacoustic diagnostic and chemotherapy against cancer.

  4. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    PubMed

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

  5. Human immunodeficiency virus testing for patient-based and population-based diagnosis.

    PubMed

    Albritton, W L; Vittinghoff, E; Padian, N S

    1996-10-01

    Laboratory testing for human immunodeficiency virus (HIV) has been introduced for individual patient-based diagnosis as well as high-risk and low-risk population-based screening. The choice of test, confirmatory algorithm, and interpretative criteria used depend on the clinical setting. In the context of general population-based testing, factors affecting test performance will have to be considered carefully in the development of testing policy. PMID:8843247

  6. Shape-based diagnosis of the aortic valve

    NASA Astrophysics Data System (ADS)

    Ionasec, Razvan Ioan; Tsymbal, Alexey; Vitanovski, Dime; Georgescu, Bogdan; Zhou, S. Kevin; Navab, Nassir; Comaniciu, Dorin

    2009-02-01

    Disorders of the aortic valve represent a common cardiovascular disease and an important public-health problem worldwide. Pathological valves are currently determined from 2D images through elaborate qualitative evalu- ations and complex measurements, potentially inaccurate and tedious to acquire. This paper presents a novel diagnostic method, which identies diseased valves based on 3D geometrical models constructed from volumetric data. A parametric model, which includes relevant anatomic landmarks as well as the aortic root and lea ets, represents the morphology of the aortic valve. Recently developed robust segmentation methods are applied to estimate the patient specic model parameters from end-diastolic cardiac CT volumes. A discriminative distance function, learned from equivalence constraints in the product space of shape coordinates, determines the corresponding pathology class based on the shape information encoded by the model. Experiments on a heterogeneous set of 63 patients aected by various diseases demonstrated the performance of our method with 94% correctly classied valves.

  7. Comparison of Predictive Models for the Early Diagnosis of Diabetes

    PubMed Central

    Jahani, Meysam

    2016-01-01

    Objectives This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). Results The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. Conclusions The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes. PMID:27200219

  8. Assessment of Differential Item Functioning under Cognitive Diagnosis Models: The DINA Model Example

    ERIC Educational Resources Information Center

    Li, Xiaomin; Wang, Wen-Chung

    2015-01-01

    The assessment of differential item functioning (DIF) is routinely conducted to ensure test fairness and validity. Although many DIF assessment methods have been developed in the context of classical test theory and item response theory, they are not applicable for cognitive diagnosis models (CDMs), as the underlying latent attributes of CDMs are…

  9. [Acute pancreatitis: guideline-based diagnosis and treatment].

    PubMed

    Tuennemann, J; Mössner, J; Beer, S

    2014-09-01

    Acute pancreatitis is most frequently of biliary or alcoholic origin and less frequently due to iatrogenic (ERCP, medication) or metabolic causes. Diagnosis is usually based on abdominal pain and elevation of serum lipase to more than three-times the normal limit. Acute pancreatitis can either resolve quickly following an oedematous swelling or present as a severe necrotizing form. A major risk is the systemic inflammatory response syndrome (SIRS), which can cause multi-organ failure. Prediction of disease course is initially difficult, thus necessitating immediate therapy and regular re-evaluation. In order to prove or exclude biliary genesis, abdominal ultrasonography should first be performed and endoscopic ultrasound may also be required. Primary therapy includes rapid and correctly dosed fluid substitution. Biliary pancreatitis requires causal treatment. In the case of cholangitis, stone extraction must be performed immediately; in the absence of cholangitis, it might be advisable to wait for spontaneous stone clearance. Timely cholecystectomy is necessary in all cases of biliary pancreatitis. PMID:25139706

  10. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

    PubMed Central

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-01-01

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. PMID:27556472

  11. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants.

    PubMed

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-08-22

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.

  12. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants.

    PubMed

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-01-01

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. PMID:27556472

  13. Fault diagnosis in spur gears based on genetic algorithm and random forest

    NASA Astrophysics Data System (ADS)

    Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  14. Evidenced-based review of clinical studies on endodontic diagnosis.

    PubMed

    2009-08-01

    The practice of endodontics requires excellence in diagnostic skills. The importance of this topic has been underscored by a recent 2008 AAE-sponsored symposium on endodontic diagnosis, which will be highlighted in a special issue of the Journal of Endodontics. In this minireview, we focus on recent clinical studies that emphasize different aspects related to the diagnosis of disorders of the pulp-dentin complex. PMID:19631854

  15. An Error Diagnosis Technique Based on Location Sets to Rectify Subcircuits

    NASA Astrophysics Data System (ADS)

    Shioki, Kosuke; Okada, Narumi; Ishihara, Toshiro; Hirose, Tetsuya; Kuroki, Nobutaka; Numa, Masahiro

    This paper presents an error diagnosis technique for incremental synthesis, called EXLLS (Extended X-algorithm for LUT-based circuit model based on Location sets to rectify Subcircuits), which rectifies five or more functional errors in the whole circuit based on location sets to rectify subcircuits. Conventional error diagnosis technique, called EXLIT, tries to rectify five or more functional errors based on incremental rectification for subcircuits. However, the solution depends on the selection and the order of modifications on subcircuits, which increases the number of locations to be changed. To overcome this problem, we propose EXLLS based on location sets to rectify subcircuits, which obtains two or more solutions by separating i) extraction of location sets to be rectified, and ii) rectification for the whole circuit based on the location sets. Thereby EXLLS can rectify five or more errors with fewer locations to change. Experimental results have shown that EXLLS reduces increase in the number of locations to be rectified with conventional technique by 90.1%.

  16. Microwave-based medical diagnosis using particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Modiri, Arezoo

    This dissertation proposes and investigates a novel architecture intended for microwave-based medical diagnosis (MBMD). Furthermore, this investigation proposes novel modifications of particle swarm optimization algorithm for achieving enhanced convergence performance. MBMD has been investigated through a variety of innovative techniques in the literature since the 1990's and has shown significant promise in early detection of some specific health threats. In comparison to the X-ray- and gamma-ray-based diagnostic tools, MBMD does not expose patients to ionizing radiation; and due to the maturity of microwave technology, it lends itself to miniaturization of the supporting systems. This modality has been shown to be effective in detecting breast malignancy, and hence, this study focuses on the same modality. A novel radiator device and detection technique is proposed and investigated in this dissertation. As expected, hardware design and implementation are of paramount importance in such a study, and a good deal of research, analysis, and evaluation has been done in this regard which will be reported in ensuing chapters of this dissertation. It is noteworthy that an important element of any detection system is the algorithm used for extracting signatures. Herein, the strong intrinsic potential of the swarm-intelligence-based algorithms in solving complicated electromagnetic problems is brought to bear. This task is accomplished through addressing both mathematical and electromagnetic problems. These problems are called benchmark problems throughout this dissertation, since they have known answers. After evaluating the performance of the algorithm for the chosen benchmark problems, the algorithm is applied to MBMD tumor detection problem. The chosen benchmark problems have already been tackled by solution techniques other than particle swarm optimization (PSO) algorithm, the results of which can be found in the literature. However, due to the relatively high level

  17. Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records

    PubMed Central

    Emir, Birol; Masters, Elizabeth T; Mardekian, Jack; Clair, Andrew; Kuhn, Max; Silverman, Stuart L

    2015-01-01

    Background Diagnosis of fibromyalgia (FM), a chronic musculoskeletal condition characterized by widespread pain and a constellation of symptoms, remains challenging and is often delayed. Methods Random forest modeling of electronic medical records was used to identify variables that may facilitate earlier FM identification and diagnosis. Subjects aged ≥18 years with two or more listings of the International Classification of Diseases, Ninth Revision, (ICD-9) code for FM (ICD-9 729.1) ≥30 days apart during the 2012 calendar year were defined as cases among subjects associated with an integrated delivery network and who had one or more health care provider encounter in the Humedica database in calendar years 2011 and 2012. Controls were without the FM ICD-9 codes. Seventy-two demographic, clinical, and health care resource utilization variables were entered into a random forest model with downsampling to account for cohort imbalances (<1% subjects had FM). Importance of the top ten variables was ranked based on normalization to 100% for the variable with the largest loss in predicting performance by its omission from the model. Since random forest is a complex prediction method, a set of simple rules was derived to help understand what factors drive individual predictions. Results The ten variables identified by the model were: number of visits where laboratory/non-imaging diagnostic tests were ordered; number of outpatient visits excluding office visits; age; number of office visits; number of opioid prescriptions; number of medications prescribed; number of pain medications excluding opioids; number of medications administered/ordered; number of emergency room visits; and number of musculoskeletal conditions. A receiver operating characteristic curve confirmed the model’s predictive accuracy using an independent test set (area under the curve, 0.810). To enhance interpretability, nine rules were developed that could be used with good predictive probability of

  18. A Note on Comparing Examinee Classification Methods for Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Huebner, Alan; Wang, Chun

    2011-01-01

    Cognitive diagnosis models have received much attention in the recent psychometric literature because of their potential to provide examinees with information regarding multiple fine-grained discretely defined skills, or attributes. This article discusses the issue of methods of examinee classification for cognitive diagnosis models, which are…

  19. Integration of Symptom Ratings from Multiple Informants in ADHD Diagnosis: A Psychometric Model with Clinical Utility

    PubMed Central

    Martel, Michelle M.; Schimmack, Ulrich; Nikolas, Molly; Nigg, Joel T.

    2015-01-01

    The Diagnostic and Statistical Manual of Mental Disorder—Fifth Edition explicitly requires that Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms should be apparent across settings, taking into account reports from multiple informants. Yet, it provides no guidelines how information from different raters should be combined in ADHD diagnosis. We examined the validity of different approaches using structural equation modeling (SEM) for multiple-informant data. Participants were 725 children, 6 to 17 years old, and their primary caregivers and teachers, recruited from the community and completing a thorough research-based diagnostic assessment, including a clinician-administered diagnostic interview, parent and teacher standardized rating scales and cognitive testing. A best-estimate ADHD diagnosis was generated by a diagnostic team. An SEM model demonstrated convergent validity among raters. We found relatively weak symptom-specific agreement among raters, suggesting that a general average scoring algorithm is preferable to symptom-specific scoring algorithms such as the “or” and “and” algorithms. Finally, to illustrate the validity of this approach, we show that averaging makes it possible to reduce the number of items from 18 items to 8 items without a significant decrease in validity. In conclusion, information from multiple raters increases the validity of ADHD diagnosis, and averaging appears to be the optimal way to integrate information from multiple raters. PMID:25730162

  20. A Hybrid Approach Using Case-Based Reasoning and Rule-Based Reasoning to Support Cancer Diagnosis: A Pilot Study.

    PubMed

    Saraiva, Renata M; Bezerra, João; Perkusich, Mirko; Almeida, Hyggo; Siebra, Clauirton

    2015-01-01

    Recently there has been an increasing interest in applying information technology to support the diagnosis of diseases such as cancer. In this paper, we present a hybrid approach using case-based reasoning (CBR) and rule-based reasoning (RBR) to support cancer diagnosis. We used symptoms, signs, and personal information from patients as inputs to our model. To form specialized diagnoses, we used rules to define the input factors' importance according to the patient's characteristics. The model's output presents the probability of the patient having a type of cancer. To carry out this research, we had the approval of the ethics committee at Napoleão Laureano Hospital, in João Pessoa, Brazil. To define our model's cases, we collected real patient data at Napoleão Laureano Hospital. To define our model's rules and weights, we researched specialized literature and interviewed health professional. To validate our model, we used K-fold cross validation with the data collected at Napoleão Laureano Hospital. The results showed that our approach is an effective CBR system to diagnose cancer. PMID:26262174

  1. Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; George, Laurence L.; Patterson-Hine, F. A.; Lum, Henry, Jr. (Technical Monitor)

    1994-01-01

    When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be

  2. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    NASA Astrophysics Data System (ADS)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  3. Clinical diagnosis support system based on case based fuzzy cognitive maps and semantic web.

    PubMed

    Douali, Nassim; De Roo, Jos; Jaulent, Marie-Christine

    2012-01-01

    Incorrect or improper diagnostic tests uses have important implications for health outcomes and costs. Clinical Decision Support Systems purports to optimize the use of diagnostic tests in clinical practice. The computerized medical reasoning should not only focus on existing medical knowledge but also on physician's previous experiences and new knowledge. Such medical knowledge is vague and defines uncertain relationships between facts and diagnosis, in this paper, Case Based Fuzzy Cognitive Maps (CBFCM) are proposed as an evolution of Fuzzy Cognitive Maps. They allow more complete representation of knowledge since case-based fuzzy rules are introduced to improve diagnosis decision. We have developed a framework for interacting with patient's data and formalizing knowledge from Guidelines in the domain of Urinary Tract Infection. The conducted study allowed us to test cognitive approaches for implementing Guidelines with Semantic Web tools. The advantage of this approach is to enable the sharing and reuse of knowledge from Guidelines, physicians experiences and simplify maintenance.

  4. Inherited platelet disorders: toward DNA-based diagnosis

    PubMed Central

    Lentaigne, Claire; Freson, Kathleen; Laffan, Michael A.; Turro, Ernest

    2016-01-01

    Variations in platelet number, volume, and function are largely genetically controlled, and many loci associated with platelet traits have been identified by genome-wide association studies (GWASs).1 The genome also contains a large number of rare variants, of which a tiny fraction underlies the inherited diseases of humans. Research over the last 3 decades has led to the discovery of 51 genes harboring variants responsible for inherited platelet disorders (IPDs). However, the majority of patients with an IPD still do not receive a molecular diagnosis. Alongside the scientific interest, molecular or genetic diagnosis is important for patients. There is increasing recognition that a number of IPDs are associated with severe pathologies, including an increased risk of malignancy, and a definitive diagnosis can inform prognosis and care. In this review, we give an overview of these disorders grouped according to their effect on platelet biology and their clinical characteristics. We also discuss the challenge of identifying candidate genes and causal variants therein, how IPDs have been historically diagnosed, and how this is changing with the introduction of high-throughput sequencing. Finally, we describe how integration of large genomic, epigenomic, and phenotypic datasets, including whole genome sequencing data, GWASs, epigenomic profiling, protein–protein interaction networks, and standardized clinical phenotype coding, will drive the discovery of novel mechanisms of disease in the near future to improve patient diagnosis and management. PMID:27095789

  5. Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

    PubMed Central

    Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Augelli, Raffaele; Dallaserra, Chiara; Puntel, Gino; Rossi, Arianna; Sala, Giuseppe; Signorini, Manuel; Spezia, Laura; Zamboni, Federico; Montemezzi, Stefania

    2016-01-01

    The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters’ predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.

  6. Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

    PubMed Central

    Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Augelli, Raffaele; Dallaserra, Chiara; Puntel, Gino; Rossi, Arianna; Sala, Giuseppe; Signorini, Manuel; Spezia, Laura; Zamboni, Federico; Montemezzi, Stefania

    2016-01-01

    The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters’ predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization. PMID:27648166

  7. Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis.

    PubMed

    Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Augelli, Raffaele; Dallaserra, Chiara; Puntel, Gino; Rossi, Arianna; Sala, Giuseppe; Signorini, Manuel; Spezia, Laura; Zamboni, Federico; Montemezzi, Stefania

    2016-08-28

    The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization. PMID:27648166

  8. Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis.

    PubMed

    Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Augelli, Raffaele; Dallaserra, Chiara; Puntel, Gino; Rossi, Arianna; Sala, Giuseppe; Signorini, Manuel; Spezia, Laura; Zamboni, Federico; Montemezzi, Stefania

    2016-08-28

    The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.

  9. Modeling the Phenotypic Architecture of Autism Symptoms from Time of Diagnosis to Age 6

    ERIC Educational Resources Information Center

    Georgiades, Stelios; Boyle, Michael; Szatmari, Peter; Hanna, Steven; Duku, Eric; Zwaigenbaum, Lonnie; Bryson, Susan; Fombonne, Eric; Volden, Joanne; Mirenda, Pat; Smith, Isabel; Roberts, Wendy; Vaillancourt, Tracy; Waddell, Charlotte; Bennett, Teresa; Elsabbagh, Mayada; Thompson, Ann

    2014-01-01

    The latent class structure of autism symptoms from the time of diagnosis to age 6 years was examined in a sample of 280 children with autism spectrum disorder. Factor mixture modeling was performed on 26 algorithm items from the Autism Diagnostic Interview-Revised at diagnosis (Time 1) and again at age 6 (Time 2). At Time 1, a…

  10. A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

    NASA Technical Reports Server (NTRS)

    Warner, James E.; Hochhalter, Jacob D.; Leser, William P.; Leser, Patrick E.; Newman, John A

    2016-01-01

    This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

  11. Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data

    ERIC Educational Resources Information Center

    de la Torre, Jimmy; Douglas, Jeffrey A.

    2008-01-01

    This paper studies three models for cognitive diagnosis, each illustrated with an application to fraction subtraction data. The objective of each of these models is to classify examinees according to their mastery of skills assumed to be required for fraction subtraction. We consider the DINA model, the NIDA model, and a new model that extends the…

  12. Diagnosis of GLDAS LSM based aridity index and dryland identification.

    PubMed

    Ghazanfari, Sadegh; Pande, Saket; Hashemy, Mehdy; Sonneveld, Ben

    2013-04-15

    The identification of dryland areas is crucial for guiding policy aimed at intervening in water-stressed areas and addressing the perennial livelihood or food insecurity of these areas. However, the prevailing aridity indices (such as UNEP aridity index) have methodological limitations that restrict their use in delineating drylands and may be insufficient for decision-making frameworks. In this study, we propose a new aridity index based on based on 3 decades of soil moisture time series by accounting for site-specific soil and vegetation that partitions precipitation into the competing demands of evaporation and runoff. Our proposed aridity index is the frequency at which the dominant soil moisture value at a location is not exceeded by the dominant soil moisture values in all of the other locations. To represent the dominant spatial template of the soil moisture conditions, we extract the first eigenfunction from the empirical orthogonal function (EOF) analysis from 3 GLDAS land surface models (LSMs): VIC, MOSAIC and NOAH at 1 × 1 degree spatial resolution. The EOF analysis reveals that the first eigenfunction explains 33%, 43% and 47% of the VIC, NOAH and MOSAIC models, respectively. We compare each LSM aridity indices with the UNEP aridity index, which is created based on LSM data forcings. The VIC aridity index displays a pattern most closely resembling that of UNEP, although all of the LSM-based indices accurately isolate the dominant dryland areas. The UNEP classification identifies portions of south-central Africa, southeastern United States and eastern India as drier than predicted by all of the LSMs. The NOAH and MOSAIC LSMs categorize portions of southwestern Africa as drier than the other two classifications, while all of the LSMs classify portions of central India as wetter than the UNEP classification. We compare all aridity maps with the long-term average NDVI values. Results show that vegetation cover in areas that the UNEP index classifies as

  13. Kinetic analyses and performance of a colloidal magnetic nanoparticle based immunoassay dedicated to allergy diagnosis.

    PubMed

    Teste, Bruno; Kanoufi, Frédéric; Descroix, Stéphanie; Poncet, Pascal; Georgelin, Thomas; Siaugue, Jean-Michel; Petr, Jan; Varenne, Anne; Hennion, Marie-Claire

    2011-07-01

    In this paper, we demonstrate the possibility to use magnetic nanoparticles as immunosupports for allergy diagnosis. Most immunoassays used for immunosupports and clinical diagnosis are based on a heterogeneous solid-phase system and suffer from mass-transfer limitation. The nanoparticles' colloidal behavior and magnetic properties bring the advantages of homogeneous immunoassay, i.e., species diffusion, and of heterogeneous immunoassay, i.e., easy separation of the immunocomplex and free forms, as well as analyte preconcentration. We thus developed a colloidal, non-competitive, indirect immunoassay using magnetic core-shell nanoparticles (MCSNP) as immunosupports. The feasibility of such an immunoassay was first demonstrated with a model antibody and described by comparing the immunocapture kinetics using macro (standard microtiter plate), micro (microparticles) and nanosupports (MCSNP). The influence of the nanosupport properties (surface chemistry, antigen density) and of the medium (ionic strength, counter ion nature) on the immunocapture efficiency and specificity was then investigated. The performances of this original MCSNP-based immunoassay were compared with a gold standard enzyme-linked immunosorbent assay (ELISA) using a microtiter plate. The capture rate of target IgG was accelerated 200-fold and a tenfold lower limit of detection was achieved. Finally, the MCSNP-based immunoassay was successfully applied to the detection of specific IgE from milk-allergic patient's sera with a lower LOD and a good agreement (CV < 6%) with the microtiter plate, confirming the great potential of this analytical platform in the field of immunodiagnosis.

  14. Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis.

    PubMed

    Xu, Yanwu; Xu, Dong; Lin, Stephen; Liu, Jiang; Cheng, Jun; Cheung, Carol Y; Aung, Tin; Wong, Tien Yin

    2011-01-01

    We propose a machine learning framework based on sliding windows for glaucoma diagnosis. In digital fundus photographs, our method automatically localizes the optic cup, which is the primary structural image cue for clinically identifying glaucoma. This localization uses a bundle of sliding windows of different sizes to obtain cup candidates in each disc image, then extracts from each sliding window a new histogram based feature that is learned using a group sparsity constraint. An epsilon-SVR (support vector regression) model based on non-linear radial basis function (RBF) kernels is used to rank each candidate, and final decisions are made with a non-maximal suppression (NMS) method. Tested on the large ORIGA(-light) clinical dataset, the proposed method achieves a 73.2% overlap ratio with manually-labeled ground-truth and a 0.091 absolute cup-to-disc ratio (CDR) error, a simple yet widely used diagnostic measure. The high accuracy of this framework on images from low-cost and widespread digital fundus cameras indicates much promise for developing practical automated/assisted glaucoma diagnosis systems. PMID:22003677

  15. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal.

    PubMed

    Cerrada, Mariela; Vinicio Sánchez, René; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-01-01

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%. PMID:26393603

  16. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal.

    PubMed

    Cerrada, Mariela; Vinicio Sánchez, René; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-09-18

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

  17. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

    PubMed Central

    Cerrada, Mariela; Sánchez, René Vinicio; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-01-01

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%. PMID:26393603

  18. Beam Diagnosis and Lattice Modeling of the Fermilab Booster

    SciTech Connect

    Huang, Xiaobiao

    2005-09-01

    A realistic lattice model is a fundamental basis for the operation of a synchrotron. In this study various beam-based measurements, including orbit response matrix (ORM) and BPM turn-by-turn data are used to verify and calibrate the lattice model of the Fermilab Booster. In the ORM study, despite the strong correlation between the gradient parameters of adjacent magnets which prevents a full determination of the model parameters, an equivalent lattice model is obtained by imposing appropriate constraints. The fitted gradient errors of the focusing magnets are within the design tolerance and the results point to the orbit offsets in the sextupole field as the source of gradient errors. A new method, the independent component analysis (ICA) is introduced to analyze multiple BPM turn-by-turn data taken simultaneously around a synchrotron. This method makes use of the redundancy of the data and the time correlation of the source signals to isolate various components, such as betatron motion and synchrotron motion, from raw BPM data. By extracting clean coherent betatron motion from noisy data and separates out the betatron normal modes when there is linear coupling, the ICA method provides a convenient means to measure the beta functions and betatron phase advances. It also separates synchrotron motion from the BPM samples for dispersion function measurement. The ICA method has the capability to separate other perturbation signals and is robust over the contamination of bad BPMs. The application of the ICA method to the Booster has enabled the measurement of the linear lattice functions which are used to verify the existing lattice model. The transverse impedance and chromaticity are measured from turn-by-turn data using high precision tune measurements. Synchrotron motion is also observed in the BPM data. The emittance growth of the Booster is also studied by data taken with ion profile monitor (IPM). Sources of emittance growth are examined and an approach to cure

  19. Irreversible entropy model for damage diagnosis in resistors

    SciTech Connect

    Cuadras, Angel Crisóstomo, Javier; Ovejas, Victoria J.; Quilez, Marcos

    2015-10-28

    We propose a method to characterize electrical resistor damage based on entropy measurements. Irreversible entropy and the rate at which it is generated are more convenient parameters than resistance for describing damage because they are essentially positive in virtue of the second law of thermodynamics, whereas resistance may increase or decrease depending on the degradation mechanism. Commercial resistors were tested in order to characterize the damage induced by power surges. Resistors were biased with constant and pulsed voltage signals, leading to power dissipation in the range of 4–8 W, which is well above the 0.25 W nominal power to initiate failure. Entropy was inferred from the added power and temperature evolution. A model is proposed to understand the relationship among resistance, entropy, and damage. The power surge dissipates into heat (Joule effect) and damages the resistor. The results show a correlation between entropy generation rate and resistor failure. We conclude that damage can be conveniently assessed from irreversible entropy generation. Our results for resistors can be easily extrapolated to other systems or machines that can be modeled based on their resistance.

  20. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

    NASA Astrophysics Data System (ADS)

    Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Ran

    2016-05-01

    Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.

  1. Personalized Clinical Diagnosis in Data Bases for Treatment Support in Phthisiology.

    PubMed

    Lugovkina, T K; Skornyakov, S N; Golubev, D N; Egorov, E A; Medvinsky, I D

    2016-01-01

    The decision-making is a key event in the clinical practice. The program products with clinical decision support models in electronic data-base as well as with fixed decision moments of the real clinical practice and treatment results are very actual instruments for improving phthisiological practice and may be useful in the severe cases caused by the resistant strains of Mycobacterium tuberculosis. The methodology for gathering and structuring of useful information (critical clinical signals for decisions) is described. Additional coding of clinical diagnosis characteristics was implemented for numeric reflection of the personal situations. The created methodology for systematization and coding Clinical Events allowed to improve the clinical decision models for better clinical results.

  2. Actuator fault tolerant multi-controller scheme using set separation based diagnosis

    NASA Astrophysics Data System (ADS)

    Seron, María M.; De Doná, José A.

    2010-11-01

    We present a fault tolerant control strategy based on a new principle for actuator fault diagnosis. The scheme employs a standard bank of observers which match the different fault situations that can occur in the plant. Each of these observers has an associated estimation error with distinctive dynamics when an estimator matches the current fault situation of the plant. Based on the information from each observer, a fault detection and isolation (FDI) module is able to reconfigure the control loop by selecting the appropriate control law from a bank of controllers, each of them designed to stabilise and achieve reference tracking for one of the given fault models. The main contribution of this article is to propose a new FDI principle which exploits the separation of sets that characterise healthy system operation from sets that characterise transitions from healthy to faulty behaviour. The new principle allows to provide pre-checkable conditions for guaranteed fault tolerance of the overall multi-controller scheme.

  3. Fault diagnosis

    NASA Technical Reports Server (NTRS)

    Abbott, Kathy

    1990-01-01

    The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to

  4. Intelligent-based Structural Damage Detection Model

    SciTech Connect

    Lee, Eric Wai Ming; Yu, K.F.

    2010-05-21

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  5. Intelligent-based Structural Damage Detection Model

    NASA Astrophysics Data System (ADS)

    Lee, Eric Wai Ming; Yu, Kin Fung

    2010-05-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  6. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease.

    PubMed

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-07-01

    Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics). PMID:27475574

  7. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease

    NASA Astrophysics Data System (ADS)

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-07-01

    Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

  8. Development of a novel multiplex beads-based assay for autoantibody detection for colorectal cancer diagnosis.

    PubMed

    Villar-Vázquez, Roi; Padilla, Guillermo; Fernández-Aceñero, María Jesús; Suárez, Adolfo; Fuente, Eduardo; Pastor, Carlos; Calero, Miguel; Barderas, Rodrigo; Casal, J Ignacio

    2016-04-01

    Humoral response in cancer patients can be used for early cancer detection. By screening high-density protein microarrays with sera from colorectal cancer (CRC) patients and controls, we identified 16 tumor-associated antigens (TAAs) exhibiting high diagnostic value. This high number of TAAs requires the development of multiplex assays combining different antigens for a faster and more accurate prediction of CRC. Here, we have developed and optimized a bead-based assay using nine selected TAAs and two controls to provide a multiplex test for early CRC diagnosis. We screened a collection of 307 CRC patients' and control sera with the beads assay to identify and validate the best TAA combination for CRC detection. The multiplex bead-based assay exhibited a similar diagnostic performance to detect the humoral response in comparison to multiple ELISA analyses. After multivariate analysis, a panel composed of GTF2B, EDIL3, HCK, PIM1, STK4, and p53, together with gender and age, was identified as the best combination of TAAs for CRC diagnosis, achieving an AUC of 89.7%, with 66% sensitivity at 90.0% fixed specificity. The model was validated using bootstrapping analysis. In summary, we have developed a novel multiplex bead assay that after validation with a larger independent cohort of sera could be utilized in a high-throughput manner for population screening to facilitate the detection of early CRC patients. PMID:26915739

  9. A GC/MS-based metabolomic approach for reliable diagnosis of phenylketonuria.

    PubMed

    Xiong, Xiyue; Sheng, Xiaoqi; Liu, Dan; Zeng, Ting; Peng, Ying; Wang, Yichao

    2015-11-01

    Although the phenylalanine/tyrosine ratio in blood has been the gold standard for diagnosis of phenylketonuria (PKU), the disadvantages of invasive sample collection and false positive error limited the application of this discriminator in the diagnosis of PKU to some extent. The aim of this study was to develop a new standard with high sensitivity and specificity in a less invasive manner for diagnosing PKU. In this study, an improved oximation-silylation method together with GC/MS was utilized to obtain the urinary metabolomic information in 47 PKU patients compared with 47 non-PKU controls. Compared with conventional oximation-silylation methods, the present approach possesses the advantages of shorter reaction time and higher reaction efficiency at a considerably lower temperature, which is beneficial to the derivatization of some thermally unstable compounds, such as phenylpyruvic acid. Ninety-seven peaks in the chromatograms were identified as endogenous metabolites by the National Institute of Standards and Technology (NIST) mass spectra library, including amino acids, organic acids, carbohydrates, amides, and fatty acids. After normalization of data using creatinine as internal standard, 19 differentially expressed compounds with p values of <0.05 were selected by independent-sample t test for the separation of the PKU group and the control group. A principal component analysis (PCA) model constructed by these differentially expressed compounds showed that the PKU group can be discriminated from the control group. Receiver-operating characteristic (ROC) analysis with area under the curve (AUC), specificity, and sensitivity of each PKU marker obtained from these differentially expressed compounds was used to evaluate the possibility of using these markers for diagnosing PKU. The largest value of AUC (0.987) with high specificity (0.936) and sensitivity (1.000) was obtained by the ROC curve of phenylacetic acid at its cutoff value (17.244 mmol/mol creatinine

  10. Fault diagnosis algorithm based on switching function for boost converters

    NASA Astrophysics Data System (ADS)

    Cho, H.-K.; Kwak, S.-S.; Lee, S.-H.

    2015-07-01

    A fault diagnosis algorithm, which is necessary for constructing a reliable power conversion system, should detect fault occurrences as soon as possible to protect the entire system from fatal damages resulting from system malfunction. In this paper, a fault diagnosis algorithm is proposed to detect open- and short-circuit faults that occur in a boost converter switch. The inductor voltage is abnormally kept at a positive DC value during a short-circuit fault in the switch or at a negative DC value during an open-circuit fault condition until the inductor current becomes zero. By employing these abnormal properties during faulty conditions, the inductor voltage is compared with the switching function to detect each fault type by generating fault alarms when a fault occurs. As a result, from the fault alarm, a decision is made in response to the fault occurrence and the fault type in less than two switching time periods using the proposed algorithm constructed in analogue circuits. In addition, the proposed algorithm has good resistivity to discontinuous current-mode operation. As a result, this algorithm features the advantages of low cost and simplicity because of its simple analogue circuit configuration.

  11. Knowledge-based approach to fault diagnosis and control in distributed process environments

    NASA Astrophysics Data System (ADS)

    Chung, Kwangsue; Tou, Julius T.

    1991-03-01

    This paper presents a new design approach to knowledge-based decision support systems for fault diagnosis and control for quality assurance and productivity improvement in automated manufacturing environments. Based on the observed manifestations, the knowledge-based diagnostic system hypothesizes a set of the most plausible disorders by mimicking the reasoning process of a human diagnostician. The data integration technique is designed to generate error-free hierarchical category files. A novel approach to diagnostic problem solving has been proposed by integrating the PADIKS (Pattern-Directed Knowledge-Based System) concept and the symbolic model of diagnostic reasoning based on the categorical causal model. The combination of symbolic causal reasoning and pattern-directed reasoning produces a highly efficient diagnostic procedure and generates a more realistic expert behavior. In addition, three distinctive constraints are designed to further reduce the computational complexity and to eliminate non-plausible hypotheses involved in the multiple disorders problem. The proposed diagnostic mechanism, which consists of three different levels of reasoning operations, significantly reduces the computational complexity in the diagnostic problem with uncertainty by systematically shrinking the hypotheses space. This approach is applied to the test and inspection data collected from a PCB manufacturing operation.

  12. [Gambling Disorder: epidemiology, diagnosis, interpretative models and intervention].

    PubMed

    Coriale, Giovanna; Ceccanti, Mauro; De Filippis, Sergio; Falletta Caravasso, Chiara; De Persis, Simone

    2015-01-01

    Gambling disorder is a frequently underdiagnosed and disabling disorder with a prevalence greatly increased in recent decades. For various reasons, only a small part of pathological gamblers seek a support making difficult an early identification and delaying the administration of appropriate treatment. In DSM-5, the disorder has been reclassified from an "Impulse-Control Disorder not elsewhere classified" to one of the "Substance-Related and Addictive Disorders" with the intention of improve the diagnosis, to better targeting the treatment and to stimulating further research efforts directed to the disorder. This article reviews assessment techniques, psychosocial and neurobiological factors in the development of pathological gambling and treatment strategies. PMID:26489071

  13. Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

    NASA Astrophysics Data System (ADS)

    Cong, Feiyun; Chen, Jin; Dong, Guangming; Pecht, Michael

    2013-04-01

    Rolling element bearing faults are among the main causes of breakdown in rotating machines. In this paper, a rolling bearing fault model is proposed based on the dynamic load analysis of a rotor-bearing system. The rotor impact factor is taken into consideration in the rolling bearing fault signal model. The defect load on the surface of the bearing is divided into two parts, the alternate load and the determinate load. The vibration response of the proposed fault signal model is investigated and the fault signal calculating equation is derived through dynamic and kinematic analysis. Outer race and inner race fault simulations are realized in the paper. The simulation process includes consideration of several parameters, such as the gravity of the rotor-bearing system, the imbalance of the rotor, and the location of the defect on the surface. The simulation results show that different amplitude contributions of the alternate load and determinate load will cause different envelope spectrum expressions. The rotating frequency sidebands will occur in the envelope spectrum in addition to the fault characteristic frequency. This appearance of sidebands will increase the difficulty of fault recognition in intelligent fault diagnosis. The experiments given in the paper have successfully verified the proposed signal model simulation results. The test rig design of the rotor bearing system simulated several operating conditions: (1) rotor bearing only; (2) rotor bearing with loader added; (3) rotor bearing with loader and rotor disk; and (4) bearing fault simulation without rotor influence. The results of the experiments have verified that the proposed rolling bearing signal model is important to the rolling bearing fault diagnosis of rotor-bearing systems.

  14. CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency

    PubMed Central

    2015-01-01

    Background The Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking. Objective We investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called “CIMIDx”, based on representative association rules that support the diagnosis of medical images (mammograms). Methods The proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype’s classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user’s perspective. Results We performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information

  15. Paper-based diagnostic devices for clinical paraquat poisoning diagnosis.

    PubMed

    Kuan, Chen-Meng; Lin, Szu-Ting; Yen, Tzung-Hai; Wang, Yu-Lin; Cheng, Chao-Min

    2016-05-01

    This article unveils the development of a paper-based analytical device designed to rapidly detect and clinically diagnose paraquat (PQ) poisoning. Using wax printing technology, we fabricated a PQ detection device by pattering hydrophobic boundaries on paper. This PQ detection device employs a colorimetric sodium dithionite assay or an ascorbic acid assay to indicate the PQ level in a buffer system or in a human serum system in 10 min. In this test, colorimetric changes, blue in color, were observable with the naked eye. By curve fitting models of sodium dithionite and ascorbic acid assays in normal human serum, we evaluated serum PQ levels for five PQ-poisoned patients before hemoperfusion (HP) treatment and one PQ-poisoned patient after HP treatment. As evidenced by similar detection outcomes, the analytical performance of our device can compete with that of the highest clinical standard, i.e., spectrophotometry, with less complicated sample preparation and with more rapid results. Accordingly, we believe that our rapid PQ detection can benefit physicians determining timely treatment strategies for PQ-poisoned patients once they are taken to hospitals, and that this approach will increase survival rates. PMID:27462379

  16. Color model comparative analysis for breast cancer diagnosis using H and E stained images

    NASA Astrophysics Data System (ADS)

    Li, Xingyu; Plataniotis, Konstantinos N.

    2015-03-01

    Digital cancer diagnosis is a research realm where signal processing techniques are used to analyze and to classify color histopathology images. Different from grayscale image analysis of magnetic resonance imaging or X-ray, colors in histopathology images convey large amount of histological information and thus play significant role in cancer diagnosis. Though color information is widely used in histopathology works, as today, there is few study on color model selections for feature extraction in cancer diagnosis schemes. This paper addresses the problem of color space selection for digital cancer classification using H and E stained images, and investigates the effectiveness of various color models (RGB, HSV, CIE L*a*b*, and stain-dependent H and E decomposition model) in breast cancer diagnosis. Particularly, we build a diagnosis framework as a comparison benchmark and take specific concerns of medical decision systems into account in evaluation. The evaluation methodologies include feature discriminate power evaluation and final diagnosis performance comparison. Experimentation on a publicly accessible histopathology image set suggests that the H and E decomposition model outperforms other assessed color spaces. For reasons behind various performance of color spaces, our analysis via mutual information estimation demonstrates that color components in the H and E model are less dependent, and thus most feature discriminate power is collected in one channel instead of spreading out among channels in other color spaces.

  17. Model based manipulator control

    NASA Technical Reports Server (NTRS)

    Petrosky, Lyman J.; Oppenheim, Irving J.

    1989-01-01

    The feasibility of using model based control (MBC) for robotic manipulators was investigated. A double inverted pendulum system was constructed as the experimental system for a general study of dynamically stable manipulation. The original interest in dynamically stable systems was driven by the objective of high vertical reach (balancing), and the planning of inertially favorable trajectories for force and payload demands. The model-based control approach is described and the results of experimental tests are summarized. Results directly demonstrate that MBC can provide stable control at all speeds of operation and support operations requiring dynamic stability such as balancing. The application of MBC to systems with flexible links is also discussed.

  18. Diagnosis-Based Risk Adjustment for Medicare Capitation Payments

    PubMed Central

    Ellis, Randall P.; Pope, Gregory C.; Iezzoni, Lisa I.; Ayanian, John Z.; Bates, David W.; Burstin, Helen; Ash, Arlene S.

    1996-01-01

    Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula. PMID:10172666

  19. A novel ELISA-based diagnosis of acquired von Willebrand disease with increased VWF proteolysis.

    PubMed

    Rauch, Antoine; Caron, Claudine; Vincent, Flavien; Jeanpierre, Emmanuelle; Ternisien, Catherine; Boisseau, Pierre; Zawadzki, Christophe; Fressinaud, Edith; Borel-Derlon, Annie; Hermoire, Sylvie; Paris, Camille; Lavenu-Bombled, Cécile; Veyradier, Agnès; Ung, Alexandre; Vincentelli, André; van Belle, Eric; Lenting, Peter J; Goudemand, Jenny; Susen, Sophie

    2016-05-01

    Von Willebrand disease-type 2A (VWD-2A) and acquired von Willebrand syndrome (AVWS) due to aortic stenosis (AS) or left ventricular assist device (LVAD) are associated with an increased proteolysis of von Willebrand factor (VWF). Analysis of VWF multimeric profile is the most sensitive way to assess such increased VWF-proteolysis. However, several technical aspects hamper a large diffusion among routine diagnosis laboratories. This makes early diagnosis and early appropriate care of increased proteolysis challenging. In this context of unmet medical need, we developed a new ELISA aiming a quick, easy and reliable assessment of VWF-proteolysis. This ELISA was assessed successively in a LVAD-model, healthy subjects (n=39), acquired TTP-patients (n=4), VWD-patients (including VWD-2A(IIA), n=22; VWD-2B, n=26; VWD-2A(IIE), n=21; and VWD-1C, n=8) and in AVWS-patients (AS, n=9; LVAD, n=9; and MGUS, n=8). A standard of VWF-proteolysis was specifically developed. Extent of VWF-proteolysis was expressed as relative percentage and as VWF proteolysis/VWF:Ag ratio. A speed-dependent increase in VWF-proteolysis was assessed in the LVAD model whereas no proteolysis was observed in TTP-patients. In VWD-patients, VWF-proteolysis was significantly increased in VWD-2A(IIA) and VWD-2B and significantly decreased in VWD-2A(IIE) versus controls (p< 0.0001). In AVWS-patients, VWF-proteolysis was significantly increased in AS- and LVAD-patients compared to controls (p< 0.0001) and not detectable in MGUS-patients. A significant increase in VWF-proteolysis was detected as soon as three hours after LVAD implantation (p< 0.01). In conclusion, we describe a new ELISA allowing a rapid and accurate diagnosis of VWF-proteolysis validated in three different clinical situations. This assay represents a helpful alternative to electrophoresis-based assay in the diagnosis and management of AVWS with increased VWF-proteolysis. PMID:26791163

  20. A Novel Matrix-Similarity Based Loss Function for Joint Regression and Classification in AD Diagnosis

    PubMed Central

    Zhu, Xiaofeng; Suk, Heung-Il; Shen, Dinggang

    2014-01-01

    Recent studies on AD/MCI diagnosis have shown that the tasks of identifying brain disease and predicting clinical scores are highly related to each other. Furthermore, it has been shown that feature selection with a manifold learning or a sparse model can handle the problems of high feature dimensionality and small sample size. However, the tasks of clinical score regression and clinical label classification were often conducted separately in the previous studies. Regarding the feature selection, to our best knowledge, most of the previous work considered a loss function defined as an element-wise difference between the target values and the predicted ones. In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix. The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i.e., predictions of clinical scores and a class label. In order to validate the effectiveness of the proposed method, we conducted experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function helped enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods. PMID:24911377

  1. A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.

    PubMed

    Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R

    2016-01-01

    A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures.

  2. A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.

    PubMed

    Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R

    2016-01-01

    A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures. PMID:27438234

  3. Waiting time disparities in breast cancer diagnosis and treatment: a population-based study in France.

    PubMed

    Molinié, F; Leux, C; Delafosse, P; Ayrault-Piault, S; Arveux, P; Woronoff, A S; Guizard, A V; Velten, M; Ganry, O; Bara, S; Daubisse-Marliac, L; Tretarre, B

    2013-10-01

    Waiting times are key indicators of a health's system performance, but are not routinely available in France. We studied waiting times for diagnosis and treatment according to patients' characteristics, tumours' characteristics and medical management options in a sample of 1494 breast cancers recorded in population-based registries. The median waiting time from the first imaging detection to the treatment initiation was 34 days. Older age, co-morbidity, smaller size of tumour, detection by organised screening, biopsy, increasing number of specimens removed, multidisciplinary consulting meetings and surgery as initial treatment were related to increased waiting times in multivariate models. Many of these factors were related to good practices guidelines. However, the strong influence of organised screening programme and the disparity of waiting times according to geographical areas were of concern. Better scheduling of diagnostic tests and treatment propositions should improve waiting times in the management of breast cancer in France.

  4. Simplified and optimized multispectral imaging for 5-ALA-based fluorescence diagnosis of malignant lesions

    PubMed Central

    Minamikawa, Takeo; Matsuo, Hisataka; Kato, Yoshiyuki; Harada, Yoshinori; Otsuji, Eigo; Yanagisawa, Akio; Tanaka, Hideo; Takamatsu, Tetsuro

    2016-01-01

    5-aminolevulinic acid (5-ALA)-based fluorescence diagnosis is now clinically applied for accurate and ultrarapid diagnosis of malignant lesions such as lymph node metastasis during surgery. 5-ALA-based diagnosis evaluates fluorescence intensity of a fluorescent metabolite of 5-ALA, protoporphyrin IX (PPIX); however, the fluorescence of PPIX is often affected by autofluorescence of tissue chromophores, such as collagen and flavins. In this study, we demonstrated PPIX fluorescence estimation with autofluorescence elimination for 5-ALA-based fluorescence diagnosis of malignant lesions by simplified and optimized multispectral imaging. We computationally optimized observation wavelength regions for the estimation of PPIX fluorescence in terms of minimizing prediction error of PPIX fluorescence intensity in the presence of typical chromophores, collagen and flavins. By using the fluorescence intensities of the optimized wavelength regions, we verified quantitative detection of PPIX fluorescence by using chemical mixtures of PPIX, flavins, and collagen. Furthermore, we demonstrated detection capability by using metastatic and non-metastatic lymph nodes of colorectal cancer patients. These results suggest the potential and usefulness of the background-free estimation method of PPIX fluorescence for 5-ALA-based fluorescence diagnosis of malignant lesions, and we expect this method to be beneficial for intraoperative and rapid cancer diagnosis. PMID:27149301

  5. Modeling and diagnosis of structural systems through sparse dynamic graphical models

    NASA Astrophysics Data System (ADS)

    Bornn, Luke; Farrar, Charles R.; Higdon, David; Murphy, Kevin P.

    2016-06-01

    Since their introduction into the structural health monitoring field, time-domain statistical models have been applied with considerable success. Current approaches still have several flaws, however, as they typically ignore the structure of the system, using individual sensor data for modeling and diagnosis. This paper introduces a Bayesian framework containing much of the previous work with autoregressive models as a special case. In addition, the framework allows for natural inclusion of structural knowledge through the form of prior distributions on the model parameters. Acknowledging the need for computational efficiency, we extend the framework through the use of decomposable graphical models, exploiting sparsity in the system to give models that are simple to fit and understand. This sparsity can be specified from knowledge of the system, from the data itself, or through a combination of the two. Using both simulated and real data, we demonstrate the capability of the model to capture the dynamics of the system and to provide clear indications of structural change and damage. We also demonstrate how learning the sparsity in the system gives insight into the structure's physical properties.

  6. Detection, modeling and matching of pleural thickenings from CT data towards an early diagnosis of malignant pleural mesothelioma

    NASA Astrophysics Data System (ADS)

    Chaisaowong, Kraisorn; Kraus, Thomas

    2014-03-01

    Pleural thickenings can be caused by asbestos exposure and may evolve into malignant pleural mesothelioma. While an early diagnosis plays the key role to an early treatment, and therefore helping to reduce morbidity, the growth rate of a pleural thickening can be in turn essential evidence to an early diagnosis of the pleural mesothelioma. The detection of pleural thickenings is today done by a visual inspection of CT data, which is time-consuming and underlies the physician's subjective judgment. Computer-assisted diagnosis systems to automatically assess pleural mesothelioma have been reported worldwide. But in this paper, an image analysis pipeline to automatically detect pleural thickenings and measure their volume is described. We first delineate automatically the pleural contour in the CT images. An adaptive surface-base smoothing technique is then applied to the pleural contours to identify all potential thickenings. A following tissue-specific topology-oriented detection based on a probabilistic Hounsfield Unit model of pleural plaques specify then the genuine pleural thickenings among them. The assessment of the detected pleural thickenings is based on the volumetry of the 3D model, created by mesh construction algorithm followed by Laplace-Beltrami eigenfunction expansion surface smoothing technique. Finally, the spatiotemporal matching of pleural thickenings from consecutive CT data is carried out based on the semi-automatic lung registration towards the assessment of its growth rate. With these methods, a new computer-assisted diagnosis system is presented in order to assure a precise and reproducible assessment of pleural thickenings towards the diagnosis of the pleural mesothelioma in its early stage.

  7. Polymerase chain reaction-based molecular diagnosis of cutaneous infections in dermatopathology.

    PubMed

    Swick, Brian L

    2012-12-01

    Conventional methods, including microscopy, culture, and serologic studies, are a mainstay in the diagnosis of cutaneous infection. However, owing to limitations associated with these techniques, such as low sensitivity for standard microscopy and in the case of culture delay in diagnosis, polymerase chain-reaction based molecular techniques have taken on an expanding role in the diagnosis of infectious processes in dermatopathology. In particular, these assays are a useful adjunct in the diagnosis of cutaneous tuberculosis, atypical mycobacterial infection, leprosy, Lyme disease, syphilis, rickettsioses, leishmaniasis, and some fungal and viral infections. Already in the case of tuberculosis and atypical mycobacterial infection, standardized polymerase chain-reaction assays are commonly used for diagnostic purposes. With time, additional molecular-based techniques will decrease in cost and gain increased standardization, thus delivering rapid diagnostic confirmation for many difficult-to-diagnose cutaneous infections from standard formalin-fixed paraffin-embedded tissue specimens.

  8. Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

    NASA Astrophysics Data System (ADS)

    Wang, Yingmin; Zhang, Fujun; Cui, Tao; Zhou, Jinlong

    2016-03-01

    Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can't be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015-0.017 5 and sample error is controlled within 0-0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis; the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

  9. Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis.

    PubMed

    Liu, Mingxia; Zhang, Daoqiang; Adeli, Ehsan; Shen, Dinggang

    2016-07-01

    Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods.

  10. Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis.

    PubMed

    Liu, Mingxia; Zhang, Daoqiang; Adeli, Ehsan; Shen, Dinggang

    2016-07-01

    Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods. PMID:26540666

  11. The patient's diagnosis: explanatory models of mental illness.

    PubMed

    Sayre, J

    2000-01-01

    The purpose of the study was to develop a grounded theory about individuals' perception of the situation of being a psychiatric patient. Thirty-five inpatients (19 males, 16 females), ages 18 to 68, in two psychiatric units of an urban, public facility were interviewed on a biweekly basis from admission to discharge. Data were analyzed using the constant comparative method, and the data indicated that participants used the basic social process of managing self-worth to deal with the stigmatizing social predicament of being a mental patient. Events occurring before admission that shaped their responses were substance abuse, medication noncompliance, and the lack of social capital, which led to norm violations and subsequent hospitalization. Six attribution categories emerged: problem, disease, crisis, punishment, ordination, and violation. Findings support the need for professionals to improve their practice by acknowledging the effects of patients' subjective assessments on their response to hospitalization and by placing more emphasis on assisting patients to deal with the stigmatizing effects of a psychiatric diagnosis.

  12. Applications of computerized interactive morphometry in pathology. II. A model for computer generated diagnosis.

    PubMed

    Marchevsky, A M; Gil, J

    1986-06-01

    We present a model for the analysis and tentative diagnosis of pathologic problems by a trained observer utilizing data generated by a video based computerized interactive morphometry system in conjunction with multivariate methods of discriminant classificatory analysis that separate classes based on unweighted numerical values and with adhoc algorithms based on hierarchical analysis. The model was tested with two diagnostic problems that could benefit from a morphometric approach: the classification of non-Hodgkin's lymphomas from routine histologic slides and the distinction of malignant mesotheliomas from benign effusions in smears prepared from pleural effusions. The touch-sensitive screen of the computerized interactive morphometry system enables trained observers to measure the real time image of profiles of interest either by using graphic standards or simply by touching the two extreme points of the diameter of interest. This procedure generates multiple data in the form of classes that can be effectively used toward the more objective discrimination of lesions of difficult classification by analysis with a combination of adhoc diagnostic algorithms and multivariate statistical methods.

  13. 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

  14. Conjunctive and Disjunctive Extensions of the Least Squares Distance Model of Cognitive Diagnosis

    ERIC Educational Resources Information Center

    Dimitrov, Dimiter M.; Atanasov, Dimitar V.

    2012-01-01

    Many models of cognitive diagnosis, including the "least squares distance model" (LSDM), work under the "conjunctive" assumption that a correct item response occurs when all latent attributes required by the item are correctly performed. This article proposes a "disjunctive" version of the LSDM under which the correct item response occurs when "at…

  15. Models of human problem solving - Detection, diagnosis, and compensation for system failures

    NASA Technical Reports Server (NTRS)

    Rouse, W. B.

    1983-01-01

    The role of the human operator as a problem solver in man-machine systems such as vehicles, process plants, transportation networks, etc. is considered. Problem solving is discussed in terms of detection, diagnosis, and compensation. A wide variety of models of these phases of problem solving are reviewed and specifications for an overall model outlined.

  16. A Binary Programming Approach to Automated Test Assembly for Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Finkelman, Matthew D.; Kim, Wonsuk; Roussos, Louis; Verschoor, Angela

    2010-01-01

    Automated test assembly (ATA) has been an area of prolific psychometric research. Although ATA methodology is well developed for unidimensional models, its application alongside cognitive diagnosis models (CDMs) is a burgeoning topic. Two suggested procedures for combining ATA and CDMs are to maximize the cognitive diagnostic index and to use a…

  17. Model-Based Systems

    NASA Technical Reports Server (NTRS)

    Frisch, Harold P.

    2007-01-01

    Engineers, who design systems using text specification documents, focus their work upon the completed system to meet Performance, time and budget goals. Consistency and integrity is difficult to maintain within text documents for a single complex system and more difficult to maintain as several systems are combined into higher-level systems, are maintained over decades, and evolve technically and in performance through updates. This system design approach frequently results in major changes during the system integration and test phase, and in time and budget overruns. Engineers who build system specification documents within a model-based systems environment go a step further and aggregate all of the data. They interrelate all of the data to insure consistency and integrity. After the model is constructed, the various system specification documents are prepared, all from the same database. The consistency and integrity of the model is assured, therefore the consistency and integrity of the various specification documents is insured. This article attempts to define model-based systems relative to such an environment. The intent is to expose the complexity of the enabling problem by outlining what is needed, why it is needed and how needs are being addressed by international standards writing teams.

  18. Application of 4G wireless network-based system for remote diagnosis and nursing of stomal complications

    PubMed Central

    Xu, Xiulian; Cao, Yingjuan; Luan, Xiaorong

    2014-01-01

    Background: This study aims to apply 4G wireless network in the remote diagnosis of stoma complications for the first time. Background: Remote diagnosis and nursing care for a variety of illnesses are urgently needed in clinical settings. Objectives: Combining with relevant clinical manifestations, an Android phone-based intelligent diagnosis system was designed to construct a universe, easy access to exploitation and human-computer interaction database and exploitation environment for applications and programs. Methods: “Production rule” and forward reasoning method were utilized to design arborescence structures and logic reasoner associated with stoma complications. Stoma physicians were responsible for delivering evaluation scores on patients’ health status using analytic hierarchy process. The emphasis of this study is to exploit an “Android phone-based system for remote diagnosis of stoma”, which is of certain universe usage. Results: Such system was tested in the Medicine Information Center of Qilu Hospital of Shandong University and initially applied in the city of De Zhou, Shandong province, China. Conclusions: These results collectively demonstrated that the system is easy to carry, of high utility and free from the limitations of wire network environment, etc. It provides clinical evidence for establishing a novel type model for the exchange between patients and physicians. PMID:25550986

  19. Experimental Evaluation of a Structure-Based Connectionist Network for Fault Diagnosis of Helicopter Gearboxes

    NASA Technical Reports Server (NTRS)

    Jammu, V. B.; Danai, K.; Lewicki, D. G.

    1998-01-01

    This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article. For this vibration data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated ba comparing them with the weights of a connectionist network trained though supervised learning. The results indicate general agreement between the modeled and experimentally obtained influences. The vibration data from the two gearboxes are also used to evaluate the performance of SBCN in fault diagnosis. The diagnostic results indicate that the SBCN is effective in directing the presence of faults and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate vibration features.

  20. Improving the Performance of the Structure-Based Connectionist Network for Diagnosis of Helicopter Gearboxes

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Danai, Koroush; Lewicki, David G.

    1996-01-01

    A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gear-box structure and characteristics of the 'features' of vibration to define the influences of faults on features. The 'structural influences' in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a connectionist network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal vibration features through the weights of SBCN to obtain fault possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, vibration data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the faults without training, and is able to improve its performance to nearly 100% after training.

  1. Hyperspectral image-based analysis of weathering sensitivity for safety diagnosis of Seongsan Ilchulbong Peak

    NASA Astrophysics Data System (ADS)

    Kim, Sungho; Kim, Heekang

    2016-10-01

    This paper presents a weathering sensitivity analysis method for the safety diagnosis of Seongsan Ilchulbong Peak using hyperspectral images. Remote sensing-based safety diagnosis is important for preventing accidents in famous mountains. A hyperspectral correlation-based method is proposed to evaluate the weathering sensitivity. The three issues are how to reduce the illumination effect, how to remove camera motion while acquiring images on a boat, and how to define the weathering sensitivity index. A novel minimum subtraction and maximum normalization (MSM-norm) method is proposed to solve the shadow and specular illumination problem. Geometrically distorted hyperspectral images are corrected by estimating the borderline of the mountain and sea surface. The final issue is solved by proposing a weathering sensitivity index (WS-Index) based on a spectral angle mapper. Real experiments on the Seongsan Ilchulbong Peak (UNESCO, World Natural Heritage) highlighted the feasibility of the proposed method in safety diagnosis by the weathering sensitivity index.

  2. Towards a physiologically based diagnosis of anorexia nervosa and bulimia nervosa.

    PubMed

    Hatch, Kent A; Spangler, Diane L; Backus, Elizabeth M; Balagna, Jonathan T; Burns, Keven S; Guzman, Brooke S; Hubbard, Matthew J; Lindblad, Stephanie L; Roeder, Beverly L; Ryther, Natalie E; Seawright, Max A; Tyau, Jaymie N; Williams, Dustin

    2007-11-01

    Diagnosis of anorexia nervosa (AN) and bulimia nervosa (BN), while including such physiological data as weight and the reproductive status of the individual, are primarily based on questionnaires and interviews that rely on self-report of both body-related concerns and eating-related behaviors. While some key components of eating disorders are psychological and thus introspective in nature, reliance on self-report for the assessment of eating-related behaviors and nutritional status lacks the objectivity that a physiologically based measure could provide. The development of a more physiologically informed diagnosis for AN and BN would provide a more objective means of diagnosing these disorders, provide a sound physiological basis for diagnosing subclinical disorders and could also aid in monitoring the effectiveness of treatments for these disorders. Empirically supported, physiologically based methods for diagnosing AN and BN are reviewed herein as well as promising physiological measures that may potentially be used in the diagnosis of AN and BN.

  3. Model Based Definition

    NASA Technical Reports Server (NTRS)

    Rowe, Sidney E.

    2010-01-01

    In September 2007, the Engineering Directorate at the Marshall Space Flight Center (MSFC) created the Design System Focus Team (DSFT). MSFC was responsible for the in-house design and development of the Ares 1 Upper Stage and the Engineering Directorate was preparing to deploy a new electronic Configuration Management and Data Management System with the Design Data Management System (DDMS) based upon a Commercial Off The Shelf (COTS) Product Data Management (PDM) System. The DSFT was to establish standardized CAD practices and a new data life cycle for design data. Of special interest here, the design teams were to implement Model Based Definition (MBD) in support of the Upper Stage manufacturing contract. It is noted that this MBD does use partially dimensioned drawings for auxiliary information to the model. The design data lifecycle implemented several new release states to be used prior to formal release that allowed the models to move through a flow of progressive maturity. The DSFT identified some 17 Lessons Learned as outcomes of the standards development, pathfinder deployments and initial application to the Upper Stage design completion. Some of the high value examples are reviewed.

  4. A novel local learning based approach with application to breast cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Xu, Songhua; Tourassi, Georgia

    2012-03-01

    In this paper, we introduce a new local learning based approach and apply it for the well-studied problem of breast cancer diagnosis using BIRADS-based mammographic features. To learn from our clinical dataset the latent relationship between these features and the breast biopsy result, our method first dynamically partitions the whole sample population into multiple sub-population groups through stochastically searching the sample population clustering space. Each encountered clustering scheme in our online searching process is then used to create a certain sample population partition plan. For every resultant sub-population group identified according to a partition plan, our method then trains a dedicated local learner to capture the underlying data relationship. In our study, we adopt the linear logistic regression model as our local learning method's base learner. Such a choice is made both due to the well-understood linear nature of the problem, which is compellingly revealed by a rich body of prior studies, and the computational efficiency of linear logistic regression--the latter feature allows our local learning method to more effectively perform its search in the sample population clustering space. Using a database of 850 biopsy-proven cases, we compared the performance of our method with a large collection of publicly available state-of-the-art machine learning methods and successfully demonstrated its performance advantage with statistical significance.

  5. Discovering associations among diagnosis groups using topic modeling.

    PubMed

    Li, Ding Cheng; Thermeau, Terry; Chute, Christopher; Liu, Hongfang

    2014-01-01

    With the rapid growth of electronic medical records (EMR), there is an increasing need of automatically extract patterns or rules from EMR data with machine learning and data mining technqiues. In this work, we applied unsupervised statistical model, latent Dirichlet allocations (LDA), to cluster patient diagnoics groups from Rochester Epidemiology Projects (REP). The initial results show that LDA holds the potential for broad application in epidemiogloy as well as other biomedical studies due to its unsupervised nature and great interpretive power. PMID:25954576

  6. Discovering Associations Among Diagnosis Groups Using Topic Modeling

    PubMed Central

    Li, Ding Cheng; Thermeau, Terry; Chute, Christopher; Liu, Hongfang

    2014-01-01

    With the rapid growth of electronic medical records (EMR), there is an increasing need of automatically extract patterns or rules from EMR data with machine learning and data mining technqiues. In this work, we applied unsupervised statistical model, latent Dirichlet allocations (LDA), to cluster patient diagnoics groups from Rochester Epidemiology Projects (REP). The initial results show that LDA holds the potential for broad application in epidemiogloy as well as other biomedical studies due to its unsupervised nature and great interpretive power. PMID:25954576

  7. Diagnosis of pneumothorax using a microwave-based detector

    NASA Astrophysics Data System (ADS)

    Ling, Geoffrey S. F.; Riechers, Ronald G., Sr.; Pasala, Krishna M.; Blanchard, Jeremy; Nozaki, Masako; Ramage, Anthony; Jackson, William; Rosner, Michael; Garcia-Pinto, Patricia; Yun, Catherine; Butler, Nathan; Riechers, Ronald G., Jr.; Williams, Daniel; Zeidman, Seth M.; Rhee, Peter; Ecklund, James M.; Fitzpatrick, Thomas; Lockhart, Stephen

    2001-08-01

    A novel method for identifying pneumothorax is presented. This method is based on a novel device that uses electromagnetic waves in the microwave radio frequency (RF) region and a modified algorithm previously used for the estimation of the angle of arrival of radar signals. In this study, we employ this radio frequency triage tool (RAFT) to the clinical condition of pneumothorax, which is a collapsed lung. In anesthetized pigs, RAFT can detect changes in the RF signature from a lung that is 20 percent or greater collapsed. These results are compared to chest x-ray. Both studies are equivalent in their ability to detect pneumothorax in pigs.

  8. Early chronic kidney disease: diagnosis, management and models of care.

    PubMed

    Wouters, Olivier J; O'Donoghue, Donal J; Ritchie, James; Kanavos, Panos G; Narva, Andrew S

    2015-08-01

    Chronic kidney disease (CKD) is prevalent in many countries, and the costs associated with the care of patients with end-stage renal disease (ESRD) are estimated to exceed US$1 trillion globally. The clinical and economic rationale for the design of timely and appropriate health system responses to limit the progression of CKD to ESRD is clear. Clinical care might improve if early-stage CKD with risk of progression to ESRD is differentiated from early-stage CKD that is unlikely to advance. The diagnostic tests that are currently used for CKD exhibit key limitations; therefore, additional research is required to increase awareness of the risk factors for CKD progression. Systems modelling can be used to evaluate the impact of different care models on CKD outcomes and costs. The US Indian Health Service has demonstrated that an integrated, system-wide approach can produce notable benefits on cardiovascular and renal health outcomes. Economic and clinical improvements might, therefore, be possible if CKD is reconceptualized as a part of primary care. This Review discusses which early CKD interventions are appropriate, the optimum time to provide clinical care, and the most suitable model of care to adopt.

  9. Bead-based microfluidic immunoassay for diagnosis of Johne's disease

    SciTech Connect

    Wadhwa, Ashutosh; Foote, Robert; Shaw, Robert W; Eda, Shigetoshi

    2012-01-01

    Microfluidics technology offers a platform for development of point-of-care diagnostic devices for various infectious diseases. In this study, we examined whether serodiagnosis of Johne s disease (JD) can be conducted in a bead-based microfluidic assay system. Magnetic micro-beads were coated with antigens of the causative agent of JD, Mycobacterium avium subsp. paratuberculosis. The antigen-coated beads were incubated with serum samples of JD-positive or negative serum samples and then with a fluorescently-labeled secondary antibody (SAB). To confirm binding of serum antibodies to the antigen, the beads were subjected to flow cytometric analysis. Different conditions (dilutions of serum and SAB, types of SAB, and types of magnetic beads) were optimized for a great degree of differentiation between the JD-negative and JD-positive samples. Using the optimized conditions, we tested a well-classified set of 155 serum samples from JD negative and JD-positive cattle by using the bead-based flow cytometric assay. Of 105 JD-positive samples, 63 samples (60%) showed higher antibody binding levels than a cut-off value determined by using antibody binding levels of JD-negative samples. In contrast, only 43-49 JD-positive samples showed higher antibody binding levels than the cut-off value when the samples were tested by commercially-available immunoassays. Microfluidic assays were performed by magnetically immobilizing a number of beads within a microchannel of a glass microchip and detecting antibody on the collected beads by laser-induced fluorescence. Antigen-coated magnetic beads treated with bovine serum sample and fluorescently-labeled SAB were loaded into a microchannel to measure the fluorescence (reflecting level of antibody binding) on the beads in the microfluidic system. When the results of five bovine serum samples obtained with the system were compared to those obtained with the flow cytometer, a high level of correlation (linear regression, r2 = 0.994) was

  10. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    NASA Technical Reports Server (NTRS)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1998-01-01

    Control of air contaminants is a crucial factor in the safety considerations of crewed space flight. Indoor air quality needs to be closely monitored during long range missions such as a Mars mission, and also on large complex space structures such as the International Space Station. This work mainly pertains to the detection and simulation of air contaminants in the space station, though much of the work is easily extended to buildings, and issues of ventilation systems. Here we propose a method with which to track the presence of contaminants using an accurate physical model, and also develop a robust procedure that would raise alarms when certain tolerance levels are exceeded. A part of this research concerns the modeling of air flow inside a spacecraft, and the consequent dispersal pattern of contaminants. Our objective is to also monitor the contaminants on-line, so we develop a state estimation procedure that makes use of the measurements from a sensor system and determines an optimal estimate of the contamination in the system as a function of time and space. The real-time optimal estimates in turn are used to detect faults in the system and also offer diagnoses as to their sources. This work is concerned with the monitoring of air contaminants aboard future generation spacecraft and seeks to satisfy NASA's requirements as outlined in their Strategic Plan document (Technology Development Requirements, 1996).

  11. Diagnosis of myocardial infarction based on lectin-induced erythrocyte agglutination: a feasibility study

    NASA Astrophysics Data System (ADS)

    Bocsi, József; Nieschke, Kathleen; Mittag, Anja; Reichert, Thomas; Laffers, Wiebke; Marecka, Monika; Pierzchalski, Arkadiusz; Piltz, Joachim; Esche, Hans-Jürgen; Wolf, Günther; Dähnert, Ingo; Baumgartner, Adolf; Tarnok, Attila

    2014-03-01

    Myocardial infarction (MI) is an acute life-threatening disease with a high incidence worldwide. Aim of this study was to test lectin-carbohydrate binding-induced red blood cell (RBC) agglutination as an innovative tool for fast, precise and cost effective diagnosis of MI. Five lectins (Ricinus communis agglutinin (RCA), Phaseolus vulgaris erythroagglutinin (PHA), Datura stramonium agglutinin (DSA), Artocarpus agglutinin (ArA), Triticum agglutinin (TA)) were tested for ability to differentiate between agglutination characteristics in patients with MI (n = 101) or angina pectoris without MI (AP) (n = 34) and healthy volunteers (HV) as control (n =68) . RBC agglutination was analyzed by light absorbance of a stirred RBC suspension in the green to red light spectrum in an agglutimeter (amtec, Leipzig, Germany) for 15 min after lectin addition. Mean cell count in aggregates was estimated from light absorbance by a mathematical model. Each lectin induced RBC agglutination. RCA led to the strongest RBC agglutination (~500 RBCs/aggregate), while the others induced substantially slower agglutination and lead to smaller aggregate sizes (5-150 RBCs/aggregate). For all analyzed lectins the lectin-induced RBC agglutination of MI or AP patients was generally higher than for HV. However, only PHA induced agglutination that clearly distinguished MI from HV. Variance analysis showed that aggregate size after 15 min. agglutination induced by PHA was significantly higher in the MI group (143 RBCs/ aggregate) than in the HV (29 RBC-s/aggregate, p = 0.000). We hypothesize that pathological changes during MI induce modification of the carbohydrate composition on the RBC membrane and thus modify RBC agglutination. Occurrence of carbohydrate-lectin binding sites on RBC membranes provides evidence about MI. Due to significant difference in the rate of agglutination between MI > HV the differentiation between these groups is possible based on PHA-induced RBC-agglutination. This novel assay

  12. Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter

    PubMed Central

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system’s efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  13. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    PubMed

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

  14. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    PubMed

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  15. Comparison of Procedure-Based and Diagnosis-Based Identifications of Severe Sepsis and Disseminated Intravascular Coagulation in Administrative Data

    PubMed Central

    Yamana, Hayato; Horiguchi, Hiromasa; Fushimi, Kiyohide; Yasunaga, Hideo

    2016-01-01

    Background Diagnoses recorded in administrative databases have limited utility for accurate identification of severe sepsis and disseminated intravascular coagulation (DIC). We evaluated the performance of alternative identification methods that use procedure records. Methods We obtained data for adult patients admitted to intensive care units in three hospitals during a 1-year period. Severe sepsis and DIC were identified by three means: laboratory data, diagnoses, and procedures. Using laboratory data as a reference, the sensitivity and specificity of procedure-based methods and diagnosis-based methods were compared. Results Of 595 intensive care unit admissions, 212 (35.6%) and 81 (13.6%) were identified as severe sepsis and DIC, respectively, using laboratory data. The sensitivity of procedure-based methods for identifying severe sepsis was 64.2%, and the specificity was 65.3%. Two diagnosis-based methods —the Angus and Martin algorithms— exhibited sensitivities of 21.7% and 14.6% and specificities of 98.7% and 99.5%, respectively, for severe sepsis. For DIC, the sensitivity of procedure-based methods was 55.6%, and the specificity was 67.1%, and the sensitivity and specificity of diagnosis-based methods were 35.8% and 98.2%, respectively. Conclusions Procedure-based methods were more sensitive and less specific than diagnosis-based methods in identifying severe sepsis and DIC. Procedure records could improve disease identification in administrative databases. PMID:27064132

  16. Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

    PubMed

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

  17. Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

    PubMed Central

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. PMID:22399894

  18. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

    PubMed Central

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. PMID

  19. Water Diagnosis in Shrimp Aquaculture based on Neural Network

    NASA Astrophysics Data System (ADS)

    Carbajal Hernández, J. J.; Sánchez Fernández, L. P.

    2007-05-01

    In many countries, the shrimp aquaculture has not advanced computational systems to supervise the artificial habitat of the farms and laboratories. A computational system of this type helps significantly to improve the environmental conditions and to elevate the production and its quality. The main idea of this study is the creation of a system using an artificial neural network (ANN), which can help to recognize patterns of problems and their evolution in shrimp aquaculture, and thus to respond with greater rapidity against the negative effects. Bad control on the shrimp artificial habitat produces organisms with high stress and as consequence losses in their defenses. It generate low nutrition, low reproduction or worse still, they prearrange to acquire lethal diseases. The proposed system helps to control this problem. Environmental variables as pH, temperature, salinity, dissolved oxygen and turbidity have an important effect in the suitable growth of the shrimps and influence in their health. However, the exact mathematical model of this relationship is unspecified; an ANN is useful for establishing a relationship between these variables and to classify a status that describes a problem into the farm. The data classification is made to recognize and to quantify two states within the pool: a) Normal: Everything is well. b) Risk: One, some or all environmental variables are outside of the allowed interval, which generates problems. The neural network will have to recognize the state and to quantify it, in others words, how normal or risky it is, which allows finding trend of the water quality. A study was developed for designing a software tool that allows recognizing the status of the water quality and control problems for the environment into the pond.

  20. Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Dong; Tsui, Kwok-Leung; Zhou, Qiang

    2016-05-01

    Rolling element bearings are commonly used in machines to provide support for rotating shafts. Bearing failures may cause unexpected machine breakdowns and increase economic cost. To prevent machine breakdowns and reduce unnecessary economic loss, bearing faults should be detected as early as possible. Because wavelet transform can be used to highlight impulses caused by localized bearing faults, wavelet transform has been widely investigated and proven to be one of the most effective and efficient methods for bearing fault diagnosis. In this paper, a new Gauss-Hermite integration based Bayesian inference method is proposed to estimate the posterior distribution of wavelet parameters. The innovations of this paper are illustrated as follows. Firstly, a non-linear state space model of wavelet parameters is constructed to describe the relationship between wavelet parameters and hypothetical measurements. Secondly, the joint posterior probability density function of wavelet parameters and hypothetical measurements is assumed to follow a joint Gaussian distribution so as to generate Gaussian perturbations for the state space model. Thirdly, Gauss-Hermite integration is introduced to analytically predict and update moments of the joint Gaussian distribution, from which optimal wavelet parameters are derived. At last, an optimal wavelet filtering is conducted to extract bearing fault features and thus identify localized bearing faults. Two instances are investigated to illustrate how the proposed method works. Two comparisons with the fast kurtogram are used to demonstrate that the proposed method can achieve better visual inspection performances than the fast kurtogram.

  1. A Physiological Signal Transmission Model to be Used for Specific Diagnosis of Cochlear Impairments

    NASA Astrophysics Data System (ADS)

    Saremi, Amin; Stenfelt, Stefan

    2011-11-01

    Many of the sophisticated characteristics of human auditory system are attributed to cochlea. Also, most of patients with a hearing loss suffer from impairments that originate from cochlea (sensorineural). Despite this, today's clinical diagnosis methods do not probe the specific origins of such cochlear lesions. The aim of this research is to introduce a physiological signal transmission model to be clinically used as a tool for diagnosis of cochlear losses. This model enables simulation of different bio-mechano-electrical processes which occur in the auditory organ of Corti inside the cochlea. What makes this model different from many available computational models is its loyalty to physiology since the ultimate goal is to model each single physiological phenomenon. This includes passive BM vibration, outer hair cells' performances such as nonlinear mechanoelectrical transduction (MET), active amplifications by somatic motor, as well as vibration to neural conversion at the inner hair cells.

  2. STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis.

    PubMed

    Zheng, Yuanjie; Englander, Sarah; Baloch, Sajjad; Zacharaki, Evangelia I; Fan, Yong; Schnall, Mitchell D; Shen, Dinggang

    2009-07-01

    The authors propose a spatiotemporal enhancement pattern (STEP) for comprehensive characterization of breast tumors in contrast-enhanced MR images. By viewing serial contrast-enhanced MR images as a single spatiotemporal image, they formulate the STEP as a combination of (1) dynamic enhancement and architectural features of a tumor, and (2) the spatial variations of pixelwise temporal enhancements. Although the latter has been widely used by radiologists for diagnostic purposes, it has rarely been employed for computer-aided diagnosis. This article presents two major contributions. First, the STEP features are introduced to capture temporal enhancement and its spatial variations. This is essentially carried out through the Fourier transformation and pharmacokinetic modeling of various temporal enhancement features, followed by the calculation of moment invariants and Gabor texture features. Second, for effectively extracting the STEP features from tumors, we develop a graph-cut based segmentation algorithm that aims at refining coarse manual segmentations of tumors. The STEP features are assessed through their diagnostic performance for differentiating between benign and malignant tumors using a linear classifier (along with a simple ranking-based feature selection) in a leave-one-out cross-validation setting. The experimental results for the proposed features exhibit superior performance, when compared to the existing approaches, with the area under the ROC curve approaching 0.97.

  3. Image-based retrieval system and computer-aided diagnosis system for renal cortical scintigraphy images

    NASA Astrophysics Data System (ADS)

    Mumcuoğlu, Erkan; Nar, Fatih; Uğur, Omer; Bozkurt, M. Fani; Aslan, Mehmet

    2008-03-01

    Cortical renal (kidney) scintigraphy images are 2D images (256x256) acquired in three projection angles (posterior, right-posterior-oblique and left-posterior-oblique). These images are used by nuclear medicine specialists to examine the functional morphology of kidney parenchyma. The main visual features examined in reading the images are: size, location, shape and activity distribution (pixel intensity distribution within the boundary of each kidney). Among the above features, activity distribution (in finding scars if any) was found to have the least interobserver reproducibility. Therefore, in this study, we developed an image-based retrieval (IBR) and a computer-based diagnosis (CAD) system, focused on this feature in particular. The developed IBR and CAD algorithms start with automatic segmentation, boundary and landmark detection. Then, shape and activity distribution features are computed. Activity distribution feature is obtained using the acquired image and image set statistics of the normal patients. Active Shape Model (ASM) technique is used for more accurate kidney segmentation. In the training step of ASM, normal patient images are used. Retrieval performance is evaluated by calculating precision and recall. CAD performance is evaluated by specificity and sensitivity. To our knowledge, this paper is the first IBR or CAD system reported in the literature on renal cortical scintigraphy images.

  4. Congenital neutropenia: diagnosis, molecular bases and patient management

    PubMed Central

    2011-01-01

    The term congenital neutropenia encompasses a family of neutropenic disorders, both permanent and intermittent, severe (<0.5 G/l) or mild (between 0.5-1.5 G/l), which may also affect other organ systems such as the pancreas, central nervous system, heart, muscle and skin. Neutropenia can lead to life-threatening pyogenic infections, acute gingivostomatitis and chronic parodontal disease, and each successive infection may leave permanent sequelae. The risk of infection is roughly inversely proportional to the circulating polymorphonuclear neutrophil count and is particularly high at counts below 0.2 G/l. When neutropenia is detected, an attempt should be made to establish the etiology, distinguishing between acquired forms (the most frequent, including post viral neutropenia and auto immune neutropenia) and congenital forms that may either be isolated or part of a complex genetic disease. Except for ethnic neutropenia, which is a frequent but mild congenital form, probably with polygenic inheritance, all other forms of congenital neutropenia are extremely rare and have monogenic inheritance, which may be X-linked or autosomal, recessive or dominant. About half the forms of congenital neutropenia with no extra-hematopoetic manifestations and normal adaptive immunity are due to neutrophil elastase (ELANE) mutations. Some patients have severe permanent neutropenia and frequent infections early in life, while others have mild intermittent neutropenia. Congenital neutropenia may also be associated with a wide range of organ dysfunctions, as for example in Shwachman-Diamond syndrome (associated with pancreatic insufficiency) and glycogen storage disease type Ib (associated with a glycogen storage syndrome). So far, the molecular bases of 12 neutropenic disorders have been identified. Treatment of severe chronic neutropenia should focus on prevention of infections. It includes antimicrobial prophylaxis, generally with trimethoprim-sulfamethoxazole, and also granulocyte

  5. A blood-based, 7-metabolite signature for the early diagnosis of Alzheimer's disease.

    PubMed

    Olazarán, Javier; Gil-de-Gómez, Luis; Rodríguez-Martín, Andrés; Valentí-Soler, Meritxell; Frades-Payo, Belén; Marín-Muñoz, Juan; Antúnez, Carmen; Frank-García, Ana; Acedo-Jiménez, Carmen; Morlán-Gracia, Lorenzo; Petidier-Torregrossa, Roberto; Guisasola, María Concepción; Bermejo-Pareja, Félix; Sánchez-Ferro, Álvaro; Pérez-Martínez, David A; Manzano-Palomo, Sagrario; Farquhar, Ruth; Rábano, Alberto; Calero, Miguel

    2015-01-01

    Accurate blood-based biomarkers of Alzheimer's disease (AD) could constitute simple, inexpensive, and non-invasive tools for the early diagnosis and treatment of this devastating neurodegenerative disease. We sought to develop a robust AD biomarker panel by identifying alterations in plasma metabolites that persist throughout the continuum of AD pathophysiology. Using a multicenter, cross-sectional study design, we based our analysis on metabolites whose levels were altered both in AD patients and in patients with amnestic mild cognitive impairment (aMCI), the earliest identifiable stage of AD. UPLC coupled to mass spectrometry was used to independently compare the levels of 495 plasma metabolites in aMCI (n = 58) and AD (n = 100) patients with those of normal cognition controls (NC, n = 93). Metabolite alterations common to both aMCI and AD patients were used to generate a logistic regression model that accurately distinguished AD from NC patients. The final panel consisted of seven metabolites: three amino acids (glutamic acid, alanine, and aspartic acid), one non-esterified fatty acid (22:6n-3, DHA), one bile acid (deoxycholic acid), one phosphatidylethanolamine [PE(36:4)], and one sphingomyelin [SM(39:1)]. Detailed analysis ruled out the influence of potential confounding variables, including comorbidities and treatments, on each of the seven biomarkers. The final model accurately distinguished AD from NC patients (AUC, 0.918). Importantly, the model also distinguished aMCI from NC patients (AUC, 0.826), indicating its potential diagnostic utility in early disease stages. These findings describe a sensitive biomarker panel that may facilitate the specific detection of early-stage AD through the analysis of plasma samples. PMID:25649659

  6. Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

    ERIC Educational Resources Information Center

    Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

    2009-01-01

    Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

  7. Investigation of candidate data structures and search algorithms to support a knowledge based fault diagnosis system

    NASA Technical Reports Server (NTRS)

    Bosworth, Edward L., Jr.

    1987-01-01

    The focus of this research is the investigation of data structures and associated search algorithms for automated fault diagnosis of complex systems such as the Hubble Space Telescope. Such data structures and algorithms will form the basis of a more sophisticated Knowledge Based Fault Diagnosis System. As a part of the research, several prototypes were written in VAXLISP and implemented on one of the VAX-11/780's at the Marshall Space Flight Center. This report describes and gives the rationale for both the data structures and algorithms selected. A brief discussion of a user interface is also included.

  8. Anatomic diagnosis of congenital heart disease. A practical approach based on the sequentiality principle.

    PubMed

    Cervantes-Salazar, Jorge; Curi-Curi, Pedro; Ramírez-Marroquín, Samuel; Calderón-Colmenero, Juan; Munoz-Castellanos, Luis

    2010-01-01

    Based on the sequentiality principle, this review proposes a practical method that allows the systematization of the anatomic diagnosis of congenital heart disease. We emphasize the need to use sequential connection between the different cardiac segments: atria, ventricles and great arteries. Five ordered steps are defined, which include determination of atrial situs and of the connection features between the ventricles and the great arteries. Related lesions and some additional special features are a second stage in the sequential analysis of congenital heart disease, which is also important for the integral diagnosis.

  9. Rapid immunohistochemistry based on alternating current electric field for intraoperative diagnosis of brain tumors.

    PubMed

    Tanino, Mishie; Sasajima, Toshio; Nanjo, Hiroshi; Akesaka, Shiori; Kagaya, Masami; Kimura, Taichi; Ishida, Yusuke; Oda, Masaya; Takahashi, Masataka; Sugawara, Taku; Yoshioka, Toshiaki; Nishihara, Hiroshi; Akagami, Yoichi; Goto, Akiteru; Minamiya, Yoshihiro; Tanaka, Shinya

    2015-01-01

    Rapid immunohistochemistry (R-IHC) can contribute to the intraoperative diagnosis of central nervous system (CNS) tumors. We have recently developed a new IHC method based on an alternating current electric field to facilitate the antigen-antibody reaction. To ensure the requirement of R-IHC for intraoperative diagnosis, 183 cases of CNS tumors were reviewed regarding the accuracy rate of diagnosis without R-IHC. The diagnostic accuracy was 90.7 % (166/183 cases) [corrected] in which definitive diagnoses were not provided in 17 cases because of the failure of glioma grading and differential diagnosis of lymphoma and glioma. To establish the clinicopathological application, R-IHC for frozen specimens was compared with standard IHC for permanent specimens. 33 gliomas were analyzed, and the Ki-67/MIB-1 indices of frozen specimens by R-IHC were consistent with the grade and statistically correlated with those of permanent specimens. Thus, R-IHC provided supportive information to determine the grade of glioma. For discrimination between glioma and lymphoma, R-IHC was able to provide clear results of CD20 and Ki-67/MIB-1 in four frozen specimens of CNS lymphoma as well as standard IHC. We conclude that the R-IHC for frozen specimens can provide important information for intraoperative diagnosis of CNS tumors.

  10. A fault diagnosis system for PV power station based on global partitioned gradually approximation method

    NASA Astrophysics Data System (ADS)

    Wang, S.; Zhang, X. N.; Gao, D. D.; Liu, H. X.; Ye, J.; Li, L. R.

    2016-08-01

    As the solar photovoltaic (PV) power is applied extensively, more attentions are paid to the maintenance and fault diagnosis of PV power plants. Based on analysis of the structure of PV power station, the global partitioned gradually approximation method is proposed as a fault diagnosis algorithm to determine and locate the fault of PV panels. The PV array is divided into 16x16 blocks and numbered. On the basis of modularly processing of the PV array, the current values of each block are analyzed. The mean current value of each block is used for calculating the fault weigh factor. The fault threshold is defined to determine the fault, and the shade is considered to reduce the probability of misjudgments. A fault diagnosis system is designed and implemented with LabVIEW. And it has some functions including the data realtime display, online check, statistics, real-time prediction and fault diagnosis. Through the data from PV plants, the algorithm is verified. The results show that the fault diagnosis results are accurate, and the system works well. The validity and the possibility of the system are verified by the results as well. The developed system will be benefit for the maintenance and management of large scale PV array.

  11. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    PubMed Central

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  12. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    PubMed

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  13. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    PubMed

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  14. Grid technology in tissue-based diagnosis: fundamentals and potential developments.

    PubMed

    Görtler, Jürgen; Berghoff, Martin; Kayser, Gian; Kayser, Klaus

    2006-01-01

    Tissue-based diagnosis still remains the most reliable and specific diagnostic medical procedure. It is involved in all technological developments in medicine and biology and incorporates tools of quite different applications. These range from molecular genetics to image acquisition and recognition algorithms (for image analysis), or from tissue culture to electronic communication services. Grid technology seems to possess all features to efficiently target specific constellations of an individual patient in order to obtain a detailed and accurate diagnosis in providing all relevant information and references. Grid technology can be briefly explained by so-called nodes that are linked together and share certain communication rules in using open standards. The number of nodes can vary as well as their functionality, depending on the needs of a specific user at a given point in time. In the beginning of grid technology, the nodes were used as supercomputers in combining and enhancing the computation power. At present, at least five different Grid functions can be distinguished, that comprise 1) computation services, 2) data services, 3) application services, 4) information services, and 5) knowledge services. The general structures and functions of a Grid are described, and their potential implementation into virtual tissue-based diagnosis is analyzed. As a result Grid technology offers a new dimension to access distributed information and knowledge and to improving the quality in tissue-based diagnosis and therefore improving the medical quality.

  15. Electrochemistry-based Battery Modeling for Prognostics

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew J.; Kulkarni, Chetan Shrikant

    2013-01-01

    Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

  16. Attention Deficit/Hyperactivity Disorder (ADHD) Diagnosis: An Activation-Executive Model

    PubMed Central

    Rodríguez, Celestino; González-Castro, Paloma; Cueli, Marisol; Areces, Debora; González-Pienda, Julio A.

    2016-01-01

    Attention deficit with, or without, hyperactivity and impulsivity (ADHD) is categorized as neuro-developmental disorder. ADHD is a common disorder in childhood and one of the most frequent conditions affecting school ages. This disorder is characterized by a persistent behavioral pattern associated with inattention, over-activity (or hyperactivity), and difficulty in controlling impulses. Current research suggests the existence of certain patterns of cortical activation and executive control, which could more objectively identify ADHD. Through the use of a risk and resilience model, this research aimed to analyze the interaction between brain activation variables (nirHEG and Q-EEG) and executive variables (Continuous performance test -CPT-) in subjects with ADHD. The study involved 499 children, 175 females (35.1%) and 324 males (64.91%); aged from 6 to 16 years (M = 11.22, SD = 1.43). Two hundred and fifty six of the children had been diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and 243 were without ADHD. For the analysis of this objective, a causal model was designed to include the following different measures of task-execution: CPT TOVA (omissions, commissions, response time, variability, D prime and the ADHD Index); electrical activity (using Q-EEG); and blood-flow oxygenation activity (using nirHEG). The causal model was tested by means of structural equation modeling (SEM). The model that had been constructed was based upon three general assumptions: (1) There are different causal models for children with ADHD and those without ADHD; (2) The activation measures influence students’ executive performance; and (3) There are measurable structural differences between the ADHD and control group models (executive and activation). In general, the results showed that: (a) activation measures influence executive patterns differently, (b) the relationship between activation variables (nirHEG and Q-EEG) depends on the brain zone being studied, (c

  17. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    PubMed

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method.

  18. The usefulness of clinical-practice-based laboratory data in facilitating the diagnosis of dengue illness.

    PubMed

    Liu, Jien-Wei; Lee, Ing-Kit; Wang, Lin; Chen, Rong-Fu; Yang, Kuender D

    2013-01-01

    Alertness to dengue and making a timely diagnosis is extremely important in the treatment of dengue and containment of dengue epidemics. We evaluated the complementary role of clinical-practice-based laboratory data in facilitating suspicion/diagnosis of dengue. One hundred overall dengue (57 dengue fever [DF] and 43 dengue hemorrhagic fever [DHF]) cases and another 100 nondengue cases (78 viral infections other than dengue, 6 bacterial sepsis, and 16 miscellaneous diseases) were analyzed. We separately compared individual laboratory variables (platelet count [PC] , prothrombin time [PT], activated partial thromboplastin time [APTT], alanine aminotransferase [ALT], and aspartate aminotransferase [AST]) and varied combined variables of DF and/or DHF cases with the corresponding ones of nondengue cases. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) in the diagnosis of DF and/or DHF were measured based on these laboratory variables. While trade-off between sensitivity and specificity, and/or suboptimal PPV/NPV was found at measurements using these variables, prolonged APTT + normal PT + PC < 100 × 10(9) cells/L had a favorable sensitivity, specificity, PPV, and NPV in diagnosis of DF and/or DHF. In conclusion, these data suggested that prolonged APTT + normal PT + PC < 100 × 10(9) cells/L is useful in evaluating the likelihood of DF and/or DHF.

  19. The Usefulness of Clinical-Practice-Based Laboratory Data in Facilitating the Diagnosis of Dengue Illness

    PubMed Central

    Liu, Jien-Wei; Lee, Ing-Kit; Wang, Lin; Chen, Rong-Fu; Yang, Kuender D.

    2013-01-01

    Alertness to dengue and making a timely diagnosis is extremely important in the treatment of dengue and containment of dengue epidemics. We evaluated the complementary role of clinical-practice-based laboratory data in facilitating suspicion/diagnosis of dengue. One hundred overall dengue (57 dengue fever [DF] and 43 dengue hemorrhagic fever [DHF]) cases and another 100 nondengue cases (78 viral infections other than dengue, 6 bacterial sepsis, and 16 miscellaneous diseases) were analyzed. We separately compared individual laboratory variables (platelet count [PC] , prothrombin time [PT], activated partial thromboplastin time [APTT], alanine aminotransferase [ALT], and aspartate aminotransferase [AST]) and varied combined variables of DF and/or DHF cases with the corresponding ones of nondengue cases. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) in the diagnosis of DF and/or DHF were measured based on these laboratory variables. While trade-off between sensitivity and specificity, and/or suboptimal PPV/NPV was found at measurements using these variables, prolonged APTT + normal PT + PC < 100 × 109 cells/L had a favorable sensitivity, specificity, PPV, and NPV in diagnosis of DF and/or DHF. In conclusion, these data suggested that prolonged APTT + normal PT + PC < 100 × 109 cells/L is useful in evaluating the likelihood of DF and/or DHF. PMID:24455678

  20. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    PubMed

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. PMID:26626623

  1. Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections

    PubMed Central

    Jrad, Nisrine; Grall-Maës, Edith; Beauseroy, Pierre

    2009-01-01

    Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant on each class and on each wrong or partially correct decision. It is based on ν-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers. PMID:19584932

  2. Gene-based multiclass cancer diagnosis with class-selective rejections.

    PubMed

    Jrad, Nisrine; Grall-Maës, Edith; Beauseroy, Pierre

    2009-01-01

    Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependent on each class and on each wrong or partially correct decision. It is based on nu-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers.

  3. A Knowledge-Based System for the Computer Assisted Diagnosis of Endoscopic Images

    NASA Astrophysics Data System (ADS)

    Kage, Andreas; Münzenmayer, Christian; Wittenberg, Thomas

    Due to the actual demographic development the use of Computer-Assisted Diagnosis (CAD) systems becomes a more important part of clinical workflows and clinical decision making. Because changes on the mucosa of the esophagus can indicate the first stage of cancerous developments, there is a large interest to detect and correctly diagnose any such lesion. We present a knowledge-based system which is able to support a physician with the interpretation and diagnosis of endoscopic images of the esophagus. Our system is designed to support the physician directly during the examination of the patient, thus prodving diagnostic assistence at the point of care (POC). Based on an interactively marked region in an endoscopic image of interest, the system provides a diagnostic suggestion, based on an annotated reference image database. Furthermore, using relevant feedback mechanisms, the results can be enhanced interactively.

  4. Plus Disease in Retinopathy of Prematurity: Pilot Study of Computer-Based and Expert Diagnosis

    PubMed Central

    Gelman, Rony; Jiang, Lei; Du, Yunling E.; Martinez-Perez, M. Elena; Flynn, John T.; Chiang, Michael F.

    2008-01-01

    Purpose To measure accuracy of plus disease diagnosis by recognized experts in retinopathy of prematurity (ROP), and to conduct a pilot study examining performance of a computer-based image analysis system, Retinal Image multiScale Analysis (RISA). Methods Twenty-two ROP experts independently interpreted a set of 34 wide-angle retinal images for presence of plus disease. A reference standard diagnosis based on expert consensus was defined for each image. Images were analyzed by the computer-based system using individual and linear combinations of system parameters for arterioles and venules: integrated curvature (IC), diameter, and tortuosity index (TI). Sensitivity, specificity, and receiver operating characteristic areas under the curve (AUC) for plus disease diagnosis compared to the reference standard were determined for each expert, as well as for the computer-based system. Results Expert sensitivity ranged from 0.308–1.000, specificity ranged from 0.571–1.000, and AUC ranged from 0.784–1.000. Among individual computer system parameters, venular IC had highest AUC (0.853). Among all computer system parameters, the linear combination of arteriolar IC, arteriolar TI, venular IC, venular diameter, and venular TI had highest AUC (0.967), which was greater than that of 18 (81.8%) of 22 experts. Conclusions Accuracy of ROP experts for plus disease diagnosis is imperfect. A computer-based image analysis system has potential to diagnose plus disease with high accuracy. Further research involving RISA system parameter cut-off values from this study are required to fully validate performance of this computer-based system compared to that of human experts. PMID:18029210

  5. Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis

    PubMed Central

    Ortiz, Andrés; Munilla, Jorge; Álvarez-Illán, Ignacio; Górriz, Juan M.; Ramírez, Javier

    2015-01-01

    Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the

  6. Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis.

    PubMed

    Ortiz, Andrés; Munilla, Jorge; Álvarez-Illán, Ignacio; Górriz, Juan M; Ramírez, Javier

    2015-01-01

    Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the

  7. Disposable dry-reagent cotton thread-based point-of-care diagnosis devices for protein and nucleic acid test.

    PubMed

    Mao, Xun; Du, Ting-E; Wang, Yiyun; Meng, Lili

    2015-03-15

    We report here for the first time by using dry-reagent cotton thread-based point-of-care diagnosis devices for low-cost, sensitive and rapid detection of a lung cancer related biomarker, squamous cell carcinoma antigen (SCCA) and a human genetic disease, hereditary tyrosinemia type I related DNA sequences. A model system comprising SCCA as an analyte and a pair of monoclonal antibodies is used to demonstrate the proof-of-concept on the dry-reagent cotton thread based immunoassay device. An enhancement protocol was employed by using two kinds of gold nanoparticle labels for SCCA test which greatly improved the sensitivity of the device. The assay avoids the multiple incubation and washing steps performed in most conventional protein analyses, which is similar with the lateral flow strip technology. Under optimal conditions, the thread based immunoassay device was capable of measuring 1ng/mL SCCA in 20min which meet the requirement for clinical diagnosis. DNA detection was successfully realized by using a novel adenosine based molecular beacon probe as reporter probes in the cotton thread based device, the linear range is 75-3000fmol which is suitable for quantitative test.

  8. Early diagnosis and Early Start Denver Model intervention in autism spectrum disorders delivered in an Italian Public Health System service

    PubMed Central

    Devescovi, Raffaella; Monasta, Lorenzo; Mancini, Alice; Bin, Maura; Vellante, Valerio; Carrozzi, Marco; Colombi, Costanza

    2016-01-01

    Background Early diagnosis combined with an early intervention program, such as the Early Start Denver Model (ESDM), can positively influence the early natural history of autism spectrum disorders. This study evaluated the effectiveness of an early ESDM-inspired intervention, in a small group of toddlers, delivered at low intensity by the Italian Public Health System. Methods Twenty-one toddlers at risk for autism spectrum disorders, aged 20–36 months, received 3 hours/wk of one-to-one ESDM-inspired intervention by trained therapists, combined with parents’ and teachers’ active engagement in ecological implementation of treatment. The mean duration of treatment was 15 months. Cognitive and communication skills, as well as severity of autism symptoms, were assessed by using standardized measures at pre-intervention (Time 0 [T0]; mean age =27 months) and post-intervention (Time 1 [T1]; mean age =42 months). Results Children made statistically significant improvements in the language and cognitive domains, as demonstrated by a series of nonparametric Wilcoxon tests for paired data. Regarding severity of autism symptoms, younger age at diagnosis was positively associated with greater improvement at post-assessment. Conclusion Our results are consistent with the literature that underlines the importance of early diagnosis and early intervention, since prompt diagnosis can reduce the severity of autism symptoms and improve cognitive and language skills in younger children. Particularly in toddlers, it seems that an intervention model based on the ESDM principles, involving the active engagement of parents and nursery school teachers, may be effective even when the individual treatment is delivered at low intensity. Furthermore, our study supports the adaptation and the positive impact of the ESDM entirely sustained by the Italian Public Health System. PMID:27366069

  9. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

    NASA Astrophysics Data System (ADS)

    Li, Chuan; Sanchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego; Vásquez, Rafael E.

    2016-08-01

    Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and vibratory measurements in such mechanical devices are all sensitive to the existence of faults. This work addresses the use of a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously. The statistical parameters of the wavelet packet transform (WPT) are first produced from the AE signal and the vibratory signal, respectively. Two deep Boltzmann machines (DBMs) are then developed for deep representations of the WPT statistical parameters. A random forest is finally suggested to fuse the outputs of the two DBMs as the integrated DRFF model. The proposed DRFF technique is evaluated using gearbox fault diagnosis experiments under different operational conditions, and achieves 97.68% of the classification rate for 11 different condition patterns. Compared to other peer algorithms, the addressed method exhibits the best performance. The results indicate that the deep learning fusion of acoustic and vibratory signals may improve fault diagnosis capabilities for gearboxes.

  10. A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

    NASA Astrophysics Data System (ADS)

    Round, A. R.; Wilkinson, S. J.; Hall, C. J.; Rogers, K. D.; Glatter, O.; Wess, T.; Ellis, I. O.

    2005-09-01

    Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.

  11. Design-based approach to ethics in computer-aided diagnosis

    NASA Astrophysics Data System (ADS)

    Collmann, Jeff R.; Lin, Jyh-Shyan; Freedman, Matthew T.; Wu, Chris Y.; Hayes, Wendelin S.; Mun, Seong K.

    1996-04-01

    A design-based approach to ethical analysis examines how computer scientists, physicians and patients make and justify choices in designing, using and reacting to computer-aided diagnosis (CADx) systems. The basic hypothesis of this research is that values are embedded in CADx systems during all phases of their development, not just retrospectively imposed on them. This paper concentrates on the work of computer scientists and physicians as they attempt to resolve central technical questions in designing clinically functional CADx systems for lung cancer and breast cancer diagnosis. The work of Lo, Chan, Freedman, Lin, Wu and their colleagues provides the initial data on which this study is based. As these researchers seek to increase the rate of true positive classifications of detected abnormalities in chest radiographs and mammograms, they explore dimensions of the fundamental ethical principal of beneficence. The training of CADx systems demonstrates the key ethical dilemmas inherent in their current design.

  12. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

    PubMed Central

    Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

    2016-01-01

    Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

  13. Next-generation sequencing-based molecular diagnosis of 82 retinitis pigmentosa probands from Northern Ireland

    PubMed Central

    Zhao, Li; Wang, Feng; Wang, Hui; Li, Yumei; Alexander, Sharon; Wang, Keqing; Willoughby, Colin E.; Zaneveld, Jacques E.; Jiang, Lichun; Soens, Zachry T.; Earle, Philip; Simpson, David

    2015-01-01

    Retinitis pigmentosa (RP) is a group of inherited retinal disorders characterized by progressive photoreceptor degeneration. An accurate molecular diagnosis is essential for disease characterization and clinical prognoses. A retinal capture panel that enriches 186 known retinal disease genes, including 55 known RP genes, was developed. Targeted next-generation sequencing was performed for a cohort of 82 unrelated RP cases from Northern Ireland, including 46 simplex cases and 36 familial cases. Disease-causing mutations were identified in 49 probands, including 28 simplex cases and 21 familial cases, achieving a solving rate of 60 %. In total, 65 pathogenic mutations were found, and 29 of these were novel. Interestingly, the molecular information of 12 probands was neither consistent with their initial inheritance pattern nor clinical diagnosis. Further clinical reassessment resulted in a refinement of the clinical diagnosis in 11 patients. This is the first study to apply next-generation sequencing-based, comprehensive molecular diagnoses to a large number of RP probands from Northern Ireland. Our study shows that molecular information can aid clinical diagnosis, potentially changing treatment options, current family counseling and management. PMID:25472526

  14. Condition-based diagnosis of mechatronic systems using a fractional calculus approach

    NASA Astrophysics Data System (ADS)

    Gutiérrez-Carvajal, Ricardo Enrique; Flávio de Melo, Leonimer; Maurício Rosário, João; Tenreiro Machado, J. A.

    2016-07-01

    While fractional calculus (FC) is as old as integer calculus, its application has been mainly restricted to mathematics. However, many real systems are better described using FC equations than with integer models. FC is a suitable tool for describing systems characterised by their fractal nature, long-term memory and chaotic behaviour. It is a promising methodology for failure analysis and modelling, since the behaviour of a failing system depends on factors that increase the model's complexity. This paper explores the proficiency of FC in modelling complex behaviour by tuning only a few parameters. This work proposes a novel two-step strategy for diagnosis, first modelling common failure conditions and, second, by comparing these models with real machine signals and using the difference to feed a computational classifier. Our proposal is validated using an electrical motor coupled with a mechanical gear reducer.

  15. Model-based software design

    NASA Technical Reports Server (NTRS)

    Iscoe, Neil; Liu, Zheng-Yang; Feng, Guohui; Yenne, Britt; Vansickle, Larry; Ballantyne, Michael

    1992-01-01

    Domain-specific knowledge is required to create specifications, generate code, and understand existing systems. Our approach to automating software design is based on instantiating an application domain model with industry-specific knowledge and then using that model to achieve the operational goals of specification elicitation and verification, reverse engineering, and code generation. Although many different specification models can be created from any particular domain model, each specification model is consistent and correct with respect to the domain model.

  16. Diagnosis Analysis of 4 TCM Patterns in Suboptimal Health Status: A Structural Equation Modelling Approach.

    PubMed

    Wang, Li-Min; Zhao, Xin; Wu, Xi-Ling; Li, Yang; Yi, Dan-Hui; Cui, Hua-Ting; Chen, Jia-Xu

    2012-01-01

    Background. We illustrated an example of structure equation modelling (SEM) in the research on SHS to explore the diagnosis of the Sub optimal health status (SHS) and provide evidence for the standardization of traditional Chinese medicine (TCM) patterns in SHS. And the diagnosis of 4 TCM patterns in SHS was evaluated in this analysis. Methods. This study assessed data on 2807 adults (aged 18 to 49) with SHS from 6 clinical centres. SEM was used to analyze the patterns of SHS in TCM. Parameters in the introduced model were estimated by the maximum likelihood method. Results. The discussed model fits the SHS data well with CFI = 0.851 and RMSEA = 0.075. The direct effect of Qi deficiency pattern on dampness pattern had the highest magnitude (value of estimate is 0.822). With regard to the construct of "Qi deficiency pattern", "fire pattern", "stagnation pattern" and "dampness pattern", the indicators with the highest load were myasthenia of limbs, vexation, deprementia, and dizziness, respectively. It had been shown that estimate factor should indicate the important degree of different symptoms in pattern. Conclusions. The weights of symptoms in the respective pattern can be statistical significant and theoretical meaningful for the 4 TCM patterns identification in SHS research. The study contributed to a theoretical framework, which has implications for the diagnosis points of SHS. PMID:22550544

  17. Model-Based Reasoning

    ERIC Educational Resources Information Center

    Ifenthaler, Dirk; Seel, Norbert M.

    2013-01-01

    In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper…

  18. Single nucleotide polymorphism-based microarray analysis for the diagnosis of hydatidiform moles

    PubMed Central

    XIE, YINGJUN; PEI, XIAOJUAN; DONG, YU; WU, HUIQUN; WU, JIANZHU; SHI, HUIJUAN; ZHUANG, XUYING; SUN, XIAOFANG; HE, JIALING

    2016-01-01

    In clinical diagnostics, single nucleotide polymorphism (SNP)-based microarray analysis enables the detection of copy number variations (CNVs), as well as copy number neutral regions, that are absent of heterozygosity throughout the genome. The aim of the present study was to evaluate the effectiveness and sensitivity of SNP-based microarray analysis in the diagnosis of hydatidiform mole (HM). By using whole-genome SNP microarray analysis, villous genotypes were detected, and the ploidy of villous tissue was determined to identify HMs. A total of 66 villous tissues and two twin tissues were assessed in the present study. Among these samples, 11 were triploid, one was tetraploid, 23 were abnormal aneuploidy, three were complete genome homozygosity, and the remaining ones were normal ploidy. The most noteworthy finding of the present study was the identification of six partial HMs and three complete HMs from those samples that were not identified as being HMs on the basis of the initial diagnosis of experienced obstetricians. This study has demonstrated that the application of an SNP-based microarray analysis was able to increase the sensitivity of diagnosis for HMs with partial and complete HMs, which makes the identification of these diseases at an early gestational age possible. PMID:27151252

  19. Principles of models based engineering

    SciTech Connect

    Dolin, R.M.; Hefele, J.

    1996-11-01

    This report describes a Models Based Engineering (MBE) philosophy and implementation strategy that has been developed at Los Alamos National Laboratory`s Center for Advanced Engineering Technology. A major theme in this discussion is that models based engineering is an information management technology enabling the development of information driven engineering. Unlike other information management technologies, models based engineering encompasses the breadth of engineering information, from design intent through product definition to consumer application.

  20. Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Wang, T.; Gu, F.; Ball, A. D.

    2013-01-01

    Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time

  1. Reclassification of clinical sleep disorders using traditional models of syndromic, neuroanatomic, pathophysiological and etiological diagnosis.

    PubMed

    Spitzer, A Robert

    2014-09-01

    more than one location, and the best predictor might be symptoms. These are issues that need to undergo careful study on a syndromic, anatomic and physiological bases. This novel model opens up new avenues for understanding central nervous system sleep disorders, providing testable hypotheses regarding diagnosis and treatment.

  2. Combining Particle Filters and Consistency-Based Approaches for Monitoring and Diagnosis of Stochastic Hybrid Systems

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Dearden, Richard; Benazera, Emmanuel

    2004-01-01

    Fault detection and isolation are critical tasks to ensure correct operation of systems. When we consider stochastic hybrid systems, diagnosis algorithms need to track both the discrete mode and the continuous state of the system in the presence of noise. Deterministic techniques like Livingstone cannot deal with the stochasticity in the system and models. Conversely Bayesian belief update techniques such as particle filters may require many computational resources to get a good approximation of the true belief state. In this paper we propose a fault detection and isolation architecture for stochastic hybrid systems that combines look-ahead Rao-Blackwellized Particle Filters (RBPF) with the Livingstone 3 (L3) diagnosis engine. In this approach RBPF is used to track the nominal behavior, a novel n-step prediction scheme is used for fault detection and L3 is used to generate a set of candidates that are consistent with the discrepant observations which then continue to be tracked by the RBPF scheme.

  3. Phenomenological models of vibration signals for condition monitoring and fault diagnosis of epicyclic gearboxes

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Liu, Zongyao; Lin, Jing; Lu, Fanbo

    2016-05-01

    Condition monitoring and fault diagnosis of epicyclic gearboxes using vibration signals are not as straightforward as that of fixed-axis gearboxes since epicyclic gearboxes behave quite differently from fixed-axis gearboxes in many aspects, like spectral structures. Aiming to present the spectral structures of vibration signals of epicyclic gearboxes, phenomenological models of vibration signals of epicyclic gearboxes are developed by algebraic equations and spectral structures of these models are deduced using Fourier series analysis. In the phenomenological models, all the possible vibration transfer paths from gear meshing points to a fixed transducer and the effects of angular shifts of planet gears on the spectral structures are considered. Accordingly, time-varying vibration transfer paths from sun-planet/ring-planet gear meshing points to the fixed transducer due to carrier rotation are given by window functions with different amplitudes. And an angular shift in one planet gear position is introduced in the process of modeling. After the theoretical derivations, three experiments are conducted on an epicyclic gearbox test rig and the spectral structures of collected vibration signals are analyzed. As a result, the effects of angular shifts of planet gears are verified, and the phenomenological models of vibration signals when a local fault occurs on the sun gear and the planet gear are validated, respectively. The experiment results demonstrate that the established phenomenological models in this paper are helpful to the condition monitoring and fault diagnosis of epicyclic gearboxes.

  4. A Novel Local Learning based Approach With Application to Breast Cancer Diagnosis

    SciTech Connect

    Xu, Songhua; Tourassi, Georgia

    2012-01-01

    The purpose of this study is to develop and evaluate a novel local learning-based approach for computer-assisted diagnosis of breast cancer. Our new local learning based algorithm using the linear logistic regression method as its base learner is described. Overall, our algorithm will perform its stochastic searching process until the total allowed computing time is used up by our random walk process in identifying the most suitable population subdivision scheme and their corresponding individual base learners. The proposed local learning-based approach was applied for the prediction of breast cancer given 11 mammographic and clinical findings reported by physicians using the BI-RADS lexicon. Our database consisted of 850 patients with biopsy confirmed diagnosis (290 malignant and 560 benign). We also compared the performance of our method with a collection of publicly available state-of-the-art machine learning methods. Predictive performance for all classifiers was evaluated using 10-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Figure 1 reports the performance of 54 machine learning methods implemented in the machine learning toolkit Weka (version 3.0). We introduced a novel local learning-based classifier and compared it with an extensive list of other classifiers for the problem of breast cancer diagnosis. Our experiments show that the algorithm superior prediction performance outperforming a wide range of other well established machine learning techniques. Our conclusion complements the existing understanding in the machine learning field that local learning may capture complicated, non-linear relationships exhibited by real-world datasets.

  5. Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Cong, Feiyun; Chen, Jin; Dong, Guangming; Zhao, Fagang

    2013-01-01

    Rolling element bearing faults are among the main causes of rotating machines breakdown. It is important to distinguish the incipient fault before the bearings step into serious failure. Based on the traditional singular value decomposition (SVD) theory, short-time matrix series (STMS) and singular value ratio (SVR) are introduced to the vibration signal processing. The proposed signal processing method is called S-SVDR (STMS based SVD method using SVR) and it has been proved to have a good local identification capability in the rolling bearing fault diagnosis. The detailed description of applying S-SVDR methods to rolling bearing fault diagnosis is given through the artificial fault signal processing in experiment 1. In experiment 2, rolling element bearing accelerated life test is performed in Hangzhou Bearing Test & Research Center (HBRC). The experimental result shows that the incipient fault can be well detected through S-SVDR processing method. However, the envelope analysis of original signal cannot detect the fault due to the existence of signal interference. A conclusion can be made that the proposed S-SVDR method has a good effect on de-noising and eliminating the signal interference of rolling bearing for the fault diagnosis.

  6. Image-based computer-assisted diagnosis system for benign paroxysmal positional vertigo

    NASA Astrophysics Data System (ADS)

    Kohigashi, Satoru; Nakamae, Koji; Fujioka, Hiromu

    2005-04-01

    We develop the image based computer assisted diagnosis system for benign paroxysmal positional vertigo (BPPV) that consists of the balance control system simulator, the 3D eye movement simulator, and the extraction method of nystagmus response directly from an eye movement image sequence. In the system, the causes and conditions of BPPV are estimated by searching the database for record matching with the nystagmus response for the observed eye image sequence of the patient with BPPV. The database includes the nystagmus responses for simulated eye movement sequences. The eye movement velocity is obtained by using the balance control system simulator that allows us to simulate BPPV under various conditions such as canalithiasis, cupulolithiasis, number of otoconia, otoconium size, and so on. Then the eye movement image sequence is displayed on the CRT by the 3D eye movement simulator. The nystagmus responses are extracted from the image sequence by the proposed method and are stored in the database. In order to enhance the diagnosis accuracy, the nystagmus response for a newly simulated sequence is matched with that for the observed sequence. From the matched simulation conditions, the causes and conditions of BPPV are estimated. We apply our image based computer assisted diagnosis system to two real eye movement image sequences for patients with BPPV to show its validity.

  7. Remote Fault Information Acquisition and Diagnosis System of the Combine Harvester Based on LabVIEW

    NASA Astrophysics Data System (ADS)

    Chen, Jin; Wu, Pei; Xu, Kai

    Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.

  8. SFT based cosmological models

    NASA Astrophysics Data System (ADS)

    Koshelev, Alexey S.

    2010-11-01

    We consider the appearance of multiple scalar fields in SFT inspired non-local models with a single scalar field at late times. In this regime all the scalar fields are free. This system minimally coupled to gravity is mainly analyzed in this note. We build one exact solution to the equations of motion. We consider an exactly solvable model which obeys a simple exact solution in the cosmological context for the Friedmann equations and that reproduces the behavior expected from SFT in the asymptotic regime.

  9. DTREE: Microcomputer-Assisted Teaching of Psychiatric Diagnosis Using a Decision Tree Model

    PubMed Central

    First, Michael B.; Williams, Janet B.W.; Spitzer, Robert L.

    1988-01-01

    DTREE has been developed to provide new clinicians with computer-assisted teaching of psychiatric diagnosis, according to the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised (DSM-III-R). At the core of DTREE is an expert system that guides the user through the process of making a diagnosis. To best model the DSM-III-R system, a decision tree design has been employed in which questions are sequentially asked about a case, with the answer determining which question is asked next. Since the primary goal of an expert system used for teaching is to be able to provide explanations of what it is doing, annotated comments and additional explanatory text are available for each question that is asked. DTREE is currently undergoing field testing in both clinical and educational settings.

  10. Model-Based Fault Diagnosis for Turboshaft Engines

    NASA Technical Reports Server (NTRS)

    Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.

    1998-01-01

    Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

  11. Practical Application of Model-based Programming and State-based Architecture to Space Missions

    NASA Technical Reports Server (NTRS)

    Horvath, Gregory; Ingham, Michel; Chung, Seung; Martin, Oliver; Williams, Brian

    2006-01-01

    A viewgraph presentation to develop models from systems engineers that accomplish mission objectives and manage the health of the system is shown. The topics include: 1) Overview; 2) Motivation; 3) Objective/Vision; 4) Approach; 5) Background: The Mission Data System; 6) Background: State-based Control Architecture System; 7) Background: State Analysis; 8) Overview of State Analysis; 9) Background: MDS Software Frameworks; 10) Background: Model-based Programming; 10) Background: Titan Model-based Executive; 11) Model-based Execution Architecture; 12) Compatibility Analysis of MDS and Titan Architectures; 13) Integrating Model-based Programming and Execution into the Architecture; 14) State Analysis and Modeling; 15) IMU Subsystem State Effects Diagram; 16) Titan Subsystem Model: IMU Health; 17) Integrating Model-based Programming and Execution into the Software IMU; 18) Testing Program; 19) Computationally Tractable State Estimation & Fault Diagnosis; 20) Diagnostic Algorithm Performance; 21) Integration and Test Issues; 22) Demonstrated Benefits; and 23) Next Steps

  12. Rule-based simulation models

    NASA Technical Reports Server (NTRS)

    Nieten, Joseph L.; Seraphine, Kathleen M.

    1991-01-01

    Procedural modeling systems, rule based modeling systems, and a method for converting a procedural model to a rule based model are described. Simulation models are used to represent real time engineering systems. A real time system can be represented by a set of equations or functions connected so that they perform in the same manner as the actual system. Most modeling system languages are based on FORTRAN or some other procedural language. Therefore, they must be enhanced with a reaction capability. Rule based systems are reactive by definition. Once the engineering system has been decomposed into a set of calculations using only basic algebraic unary operations, a knowledge network of calculations and functions can be constructed. The knowledge network required by a rule based system can be generated by a knowledge acquisition tool or a source level compiler. The compiler would take an existing model source file, a syntax template, and a symbol table and generate the knowledge network. Thus, existing procedural models can be translated and executed by a rule based system. Neural models can be provide the high capacity data manipulation required by the most complex real time models.

  13. Retinoblastoma diagnosis: a proposal based on the experience of Centro Infantil Boldrini, Brazil.

    PubMed

    Palazzi, Maristela A; Stephan, Celso; Brandalise, Silvia R; Aguiar, Simone Dos Santos

    2013-08-01

    Advanced disease is a risk factor for eye loss in patients with retinoblastoma (RB). We still record critical rates of enucleation, especially for unilateral RB due to advanced stages of disease at diagnosis. This retrospective study of 223 RB patient records referred to treatment at Centro Infantil Boldrini, Brazil, between 1978 and 2008, showed that 176 patients (79%) presented intraocular tumors while 47 (21%) already had extraocular involvement. At the time of diagnosis, the age of patients was 26.2 months in the group that had enucleated eyes and 13.7 months in the group that preserved both eyes. Under a multiple logistic regression model, familial history (OR = 0.195; p = .01) and age at diagnosis in months (OR = 1.047; p = .04) were significantly correlated with enucleation. Strategies to early detect RB must be changed in order to offer better chances of ocular preservation with visual function. Authors propose a systematic referral of all children to the ophthalmologist for an indirect ophthalmoscopy once a year in the first two years of life, as a measure to be adopted by all pediatricians in daily routine to early detect the tumor.

  14. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images.

    PubMed

    Kowal, Marek; Filipczuk, Paweł; Obuchowicz, Andrzej; Korbicz, Józef; Monczak, Roman

    2013-10-01

    Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-scale screening. One of the largest challenges in the automatic analysis of cytological images is the segmentation of nuclei. In this study, four different clustering algorithms are tested and compared in the task of fast nuclei segmentation. K-means, fuzzy C-means, competitive learning neural networks and Gaussian mixture models were incorporated for clustering in the color space along with adaptive thresholding in grayscale. These methods were applied in a medical decision support system for breast cancer diagnosis, where the cases were classified as either benign or malignant. In the segmented nuclei, 42 morphological, topological and texture features were extracted. Then, these features were used in a classification procedure with three different classifiers. The system was tested for classification accuracy by means of microscopic images of fine needle breast biopsies. In cooperation with the Regional Hospital in Zielona Góra, 500 real case medical images from 50 patients were collected. The acquired classification accuracy was approximately 96-100%, which is very promising and shows that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis. PMID:24034748

  15. Identifying Model-Based Reconfiguration Goals through Functional Deficiencies

    NASA Technical Reports Server (NTRS)

    Benazera, Emmanuel; Trave-Massuyes, Louise

    2004-01-01

    Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.

  16. Intelligent model-based diagnostics for vehicle health management

    NASA Astrophysics Data System (ADS)

    Luo, Jianhui; Tu, Fang; Azam, Mohammad S.; Pattipati, Krishna R.; Willett, Peter K.; Qiao, Liu; Kawamoto, Masayuki

    2003-08-01

    The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed. In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.

  17. A dynamic integrated fault diagnosis method for power transformers.

    PubMed

    Gao, Wensheng; Bai, Cuifen; Liu, Tong

    2015-01-01

    In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.

  18. A Dynamic Integrated Fault Diagnosis Method for Power Transformers

    PubMed Central

    Gao, Wensheng; Liu, Tong

    2015-01-01

    In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified. PMID:25685841

  19. Diffuse lung disease of infancy: a pattern-based, algorithmic approach to histological diagnosis.

    PubMed

    Armes, Jane E; Mifsud, William; Ashworth, Michael

    2015-02-01

    Diffuse lung disease (DLD) of infancy has multiple aetiologies and the spectrum of disease is substantially different from that seen in older children and adults. In many cases, a specific diagnosis renders a dire prognosis for the infant, with profound management implications. Two recently published series of DLD of infancy, collated from the archives of specialist centres, indicate that the majority of their cases were referred, implying that the majority of biopsies taken for DLD of infancy are first received by less experienced pathologists. The current literature describing DLD of infancy takes a predominantly aetiological approach to classification. We present an algorithmic, histological, pattern-based approach to diagnosis of DLD of infancy, which, with the aid of appropriate multidisciplinary input, including clinical and radiological expertise and ancillary diagnostic studies, may lead to an accurate and useful interim report, with timely exclusion of inappropriate diagnoses. Subsequent referral to a specialist centre for confirmatory diagnosis will be dependent on the individual case and the decision of the multidisciplinary team.

  20. Socio-cultural and Knowledge-Based Barriers to Tuberculosis Diagnosis for Women in Bhopal, India

    PubMed Central

    McArthur, Evonne; Bali, Surya; Khan, Azim A.

    2016-01-01

    Background: In India, only one woman is diagnosed with tuberculosis (TB) for every 2.4 men. Previous studies have indicated gender disparities in care-seeking behavior and TB diagnosis; however, little is known about the specific barriers women face. Objectives: This study aimed to characterize socio-cultural and knowledge-based barriers that affected TB diagnosis for women in Bhopal, India. Materials and Methods: In-depth interviews were conducted with 13 affected women and 6 health-care workers. The Bhopal Diagnostic Microscopy Laboratory Register (n = 121) and the Bhopal district report (n = 261) were examined for diagnostic and care-seeking trends. Results: Women, especially younger women, faced socio-cultural barriers and stigma, causing many to hide their symptoms. Older women had little awareness about TB. Women often sought treatment from private practitioners, resulting in delayed diagnosis. Conclusions: Understanding these diagnostic and help-seeking behaviors barriers for women is critical for development of a gender-sensitive TB control program. PMID:26917876

  1. Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the “i-ROP” System and Image Features Associated With Expert Diagnosis

    PubMed Central

    Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J. Peter; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir; Jonas, Karyn; Chan, R. V. Paul; Ostmo, Susan; Chiang, Michael F.

    2015-01-01

    Purpose We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. Methods A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the “i-ROP” system. Results Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). Conclusions This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Translational Relevance Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists. PMID:26644965

  2. Extraction Method of Brain Regions Using Balloon models for Diagnosis Support of Alzheimer-Type Dementia

    NASA Astrophysics Data System (ADS)

    Ito, Momoyo; Nishida, Makoto; Namura, Ikuro

    We intend to construct an image diagnosis support system for Alzheimer-type Dementia (ATD) that extracts temporal lobe regions and an intracranial region as interest regions from a T2-weighted MR frontal image and uses the cerebral atrophy rates at the regions of interest. In this paper, we specifically discuss extraction of regions of interest. The proposed method consists of three steps. First, we emphasis features of an obscure T2-weighted brain image. Second, we set a first contour that approximates a shape of the temporal lobe region to a triangle and apply Balloon models with the added presser force that push the initial contour outside in order to extract a temporal lobe region. Finally, we extract an intracranial region using extracted temporal lobe regions. Our proposed method can extract regions of interest along individual brain features by only a few interactions with three points. We demonstrate the potential of our method using actual diagnosis images. Moreover, we show a possibility to use of atrophy rate at the regions of interest for diagnosis support of ATD.

  3. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    NASA Astrophysics Data System (ADS)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-12-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  4. Computer-aided diagnosis of peripheral soft tissue masses based on ultrasound imaging.

    PubMed

    Chiou, Hong-Jen; Chen, Chih-Yen; Liu, Tzu-Chiang; Chiou, See-Ying; Wang, Hsin-Kai; Chou, Yi-Hong; Chiang, Huihua Kenny

    2009-07-01

    Medical ultrasound (US) has been widely used for distinguishing benign from malignant peripheral soft tissue tumors. However, diagnosis by US is subjective and depends on the experience of the radiologists. The rarity of peripheral soft tissue tumors can make them easily neglected and this frequently leads to delayed diagnosis, which results in a much higher death rate than with other tumors. In this paper, we developed a computer-aided diagnosis (CAD) system to diagnose peripheral soft tissue masses on US images. We retrospectively evaluated 49 cases of pathologically proven peripheral soft tissue masses (32 benign, 17 malignant). The proposed CAD system includes three main procedures: image pre-processing and region-of-interest (ROI) segmentation, feature extraction and statistics-based discriminant analysis (DA). We developed a depth-normalization factor (DNF) to compensate for the influence of the depth setting on the apparent size of the ROI. After image pre-processing and normalization, five features, namely area (A), boundary transition ratio (T), circularity (C), high intensity spots (H) and uniformity (U), were extracted from the US images. A DA function was then employed to analyze these features. A CAD algorithm was then devised for differentiating benign from malignant masses. The CAD system achieved an accuracy of 87.8%, a sensitivity of 88.2%, a specificity of 87.5%, a positive predictive value (PPV) 78.9% and a negative predictive value (NPV) 93.3%. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out peripheral soft tissue mass diagnosis.

  5. A java-based application for differential diagnosis of hematopoietic neoplasms using immunophenotyping by flow cytometry.

    PubMed

    Nguyen, A N; Milam, J D; Johnson, K A; Banez, E I

    2000-07-01

    We describe the implementation of a Java-based application for differential diagnosis of hematopoietic neoplasms using immunophenotyping by flow cytometry. The current version of this Java applet includes the knowledge-base for 33 hematopoietic neoplasms and 43 diagnostic immunophenotyping markers. Java, a new object-oriented computing language, helps facilitate development of this applet, a platform-independent module that can be implemented on the World Wide Web. As the Web rapidly becomes more accessible to users around the world, Web-based software may eventually form the core of decision-support systems in clinical settings. Java-based applications, such as the one described in this paper, are expected to contribute significantly in this area.

  6. Haplotype-based approach for noninvasive prenatal diagnosis of congenital adrenal hyperplasia by maternal plasma DNA sequencing.

    PubMed

    Ma, Dingyuan; Ge, Huijuan; Li, Xuchao; Jiang, Tao; Chen, Fang; Zhang, Yanyan; Hu, Ping; Chen, Shengpei; Zhang, Jingjing; Ji, Xiuqing; Xu, Xun; Jiang, Hui; Chen, Minfeng; Wang, Wei; Xu, Zhengfeng

    2014-07-10

    Prenatal diagnosis of congenital adrenal hyperplasia (CAH) is of clinical significance because in utero treatment is available to prevent virilization of an affected female fetus. However, traditional prenatal diagnosis of CAH relies on genetic testing of fetal genomic DNA obtained using amniocentesis or chorionic villus sampling, which is associated with an increased risk of miscarriage. The aim of this study was to demonstrate the feasibility of a new haplotype-based approach for the noninvasive prenatal testing of CAH due to 21-hydroxylase deficiency. Parental haplotypes were constructed using target-region sequencing data of the parents and the proband. With the assistance of the parental haplotypes, we recovered fetal haplotypes using a hidden Markov model (HMM) through maternal plasma DNA sequencing. In the genomic region around the CYP21A2 gene, the fetus inherited the paternal haplotype '0' alleles linked to the mutant CYP21A2 gene, but the maternal haplotype '1' alleles linked to the wild-type gene. The fetus was predicted to be an unaffected carrier of CAH, which was confirmed by genetic analysis of fetal genomic DNA from amniotic fluid cells. This method was further validated by comparing the inferred SNP genotypes with the direct sequencing data of fetal genomic DNA. The result showed an accuracy of 96.41% for the inferred maternal alleles and an accuracy of 97.81% for the inferred paternal alleles. The haplotype-based approach is feasible for noninvasive prenatal testing of CAH.

  7. Nanoparticle-based probes to enable noninvasive imaging of proteolytic activity for cancer diagnosis.

    PubMed

    Anani, Tareq; Panizzi, Peter; David, Allan E

    2016-08-01

    Proteases play a key role in tumor biology, with high expression levels often correlating with poor prognosis for cancer patients - making them excellent disease markers for tumor diagnosis. Despite their significance, quantifying proteolytic activity in vivo remains a challenge. Nanoparticles, with their ability to serve as scaffolds having unique chemical, optical and magnetic properties, offer the promise of merging diagnostic medicine with material engineering. Such nanoparticles can interact preferentially with proteases enriched in tumors, providing the ability to assess disease state in a noninvasive and spatiotemporal manner. We review recent advances in the development of nanoparticles for imaging and quantification of proteolytic activity in tumor models, and prognosticate future advancements. PMID:27465386

  8. Determination of a Change Point in the Age at Diagnosis of Breast Cancer Using a Survival Model.

    PubMed

    Abdollahi, Mahbubeh; Hajizadeh, Ebrahim; Baghestani, Ahmad Reza; Haghighat, Shahpar

    2016-01-01

    Breast cancer, the second cause of cancer-related death after lung cancer and the most common cancer in women after skin cancer, is curable if detected in early stages of clinical presentation. Knowledge as to any age cut-off points which might have significance for prognostic groups is important in screening and treatment planning. Therefore, determining a change-point could improve resource allocation. This study aimed to determine if a change point for survival might exist in the age of breast cancer diagnosis. This study included 568 cases of breast cancer that were registered in Breast Cancer Research Center, Tehran, Iran, during the period 1986-2006 and were followed up to 2012. In the presence of curable cases of breast cancer, a change point in the age of breast cancer diagnosis was estimated using a mixture survival cure model. The data were analyzed using SPSS (versions 20) and R (version 2.15.0) software. The results revealed that a change point in the age of breast cancer diagnosis was at 50 years age. Based on our estimation, 35% of the patients diagnosed with breast cancer at age less than or equal to 50 years of age were cured while the figure was 57% for those diagnosed after 50 years of age. Those in the older age group had better survival compared to their younger counterparts during 12 years of follow up. Our results suggest that it is better to estimate change points in age for cancers which are curable in early stages using survival cure models, and that the cure rate would increase with timely screening for breast cancer.

  9. Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

    PubMed Central

    2012-01-01

    Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance. PMID:23253202

  10. Bond graph modeling and experimental verification of a novel scheme for fault diagnosis of rolling element bearings in special operating conditions

    NASA Astrophysics Data System (ADS)

    Mishra, C.; Samantaray, A. K.; Chakraborty, G.

    2016-09-01

    Vibration analysis for diagnosis of faults in rolling element bearings is complicated when the rotor speed is variable or slow. In the former case, the time interval between the fault-induced impact responses in the vibration signal are non-uniform and the signal strength is variable. In the latter case, the fault-induced impact response strength is weak and generally gets buried in the noise, i.e. noise dominates the signal. This article proposes a diagnosis scheme based on a combination of a few signal processing techniques. The proposed scheme initially represents the vibration signal in terms of uniformly resampled angular position of the rotor shaft by using the interpolated instantaneous angular position measurements. Thereafter, intrinsic mode functions (IMFs) are generated through empirical mode decomposition (EMD) of resampled vibration signal which is followed by thresholding of IMFs and signal reconstruction to de-noise the signal and envelope order tracking to diagnose the faults. Data for validating the proposed diagnosis scheme are initially generated from a multi-body simulation model of rolling element bearing which is developed using bond graph approach. This bond graph model includes the ball and cage dynamics, localized fault geometry, contact mechanics, rotor unbalance, and friction and slip effects. The diagnosis scheme is finally validated with experiments performed with the help of a machine fault simulator (MFS) system. Some fault scenarios which could not be experimentally recreated are then generated through simulations and analyzed through the developed diagnosis scheme.

  11. Model based vibration monitoring

    SciTech Connect

    Esat, I.; Paya, B.; Badi, M.N.M.

    1996-11-01

    The principal source of vibratory excitation of gear system is the unsteady component of the relative angular motion of pair of meshing spur gears. This vibratory excitation is described by the transmission error. The transmission error present itself as a varying force at the contact point of the meshing gear teeth. The varying force is also influenced by the varying tooth stiffness due to change of orientation of teeth relative to each other, during the contact phase of each pair. Such a varying force produces both lateral and torsional excitation to the gear system. This paper presents analytical formulation of a simple two meshing spur gear system as a three mass system (18 DOF). The mathematical model also incorporates the analytical formulation of the tooth stiffness. The analytical results are compared with the experimental results. At this stage of analysis the procedure developed for handling the nonlinear influences of the tooth geometry is not fully implemented and the tooth stiffness taken as a constant value representing the average tooth stiffness. The comparison between the analytical and experimental results are encouraging as three main frequency obtained from FFT of the experimental results correlates very closely with the analytical results.

  12. Diagnosis of disc herniation based on classifiers and features generated from spine MR images

    NASA Astrophysics Data System (ADS)

    Koh, Jaehan; Chaudhary, Vipin; Dhillon, Gurmeet

    2010-03-01

    In recent years the demand for an automated method for diagnosis of disc abnormalities has grown as more patients suffer from lumbar disorders and radiologists have to treat more patients reliably in a limited amount of time. In this paper, we propose and compare several classifiers that diagnose disc herniation, one of the common problems of the lumbar spine, based on lumbar MR images. Experimental results on a limited data set of 68 clinical cases with 340 lumbar discs show that our classifiers can diagnose disc herniation with 97% accuracy.

  13. The Era of Molecular and Other Non-Culture-Based Methods in Diagnosis of Sepsis

    PubMed Central

    Mancini, Nicasio; Carletti, Silvia; Ghidoli, Nadia; Cichero, Paola; Burioni, Roberto; Clementi, Massimo

    2010-01-01

    Summary: Sepsis, a leading cause of morbidity and mortality throughout the world, is a clinical syndrome with signs and symptoms relating to an infectious event and the consequent important inflammatory response. From a clinical point of view, sepsis is a continuous process ranging from systemic inflammatory response syndrome (SIRS) to multiple-organ-dysfunction syndrome (MODS). Blood cultures are the current “gold standard” for diagnosis, and they are based on the detection of viable microorganisms present in blood. However, on some occasions, blood cultures have intrinsic limitations in terms of sensitivity and rapidity, and it is not expected that these drawbacks will be overcome by significant improvements in the near future. For these principal reasons, other approaches are therefore needed in association with blood culture to improve the overall diagnostic yield for septic patients. These considerations have represented the rationale for the development of highly sensitive and fast laboratory methods. This review addresses non-culture-based techniques for the diagnosis of sepsis, including molecular and other non-culture-based methods. In particular, the potential clinical role for the sensitive and rapid detection of bacterial and fungal DNA in the development of new diagnostic algorithms is discussed. PMID:20065332

  14. Model-Based Method for Sensor Validation

    NASA Technical Reports Server (NTRS)

    Vatan, Farrokh

    2012-01-01

    Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

  15. Improving the Accuracy of Early Diagnosis of Thyroid Nodule Type Based on the SCAD Method.

    PubMed

    Shahraki, Hadi Raeisi; Pourahmad, Saeedeh; Paydar, Shahram; Azad, Mohsen

    2016-01-01

    Although early diagnosis of thyroid nodule type is very important, the diagnostic accuracy of standard tests is a challenging issue. We here aimed to find an optimal combination of factors to improve diagnostic accuracy for distinguishing malignant from benign thyroid nodules before surgery. In a prospective study from 2008 to 2012, 345 patients referred for thyroidectomy were enrolled. The sample size was split into a training set and testing set as a ratio of 7:3. The former was used for estimation and variable selection and obtaining a linear combination of factors. We utilized smoothly clipped absolute deviation (SCAD) logistic regression to achieve the sparse optimal combination of factors. To evaluate the performance of the estimated model in the testing set, a receiver operating characteristic (ROC) curve was utilized. The mean age of the examined patients (66 male and 279 female) was 40.9 ± 13.4 years (range 15- 90 years). Some 54.8% of the patients (24.3% male and 75.7% female) had benign and 45.2% (14% male and 86% female) malignant thyroid nodules. In addition to maximum diameters of nodules and lobes, their volumes were considered as related factors for malignancy prediction (a total of 16 factors). However, the SCAD method estimated the coefficients of 8 factors to be zero and eliminated them from the model. Hence a sparse model which combined the effects of 8 factors to distinguish malignant from benign thyroid nodules was generated. An optimal cut off point of the ROC curve for our estimated model was obtained (p=0.44) and the area under the curve (AUC) was equal to 77% (95% CI: 68%-85%). Sensitivity, specificity, positive predictive value and negative predictive values for this model were 70%, 72%, 71% and 76%, respectively. An increase of 10 percent and a greater accuracy rate in early diagnosis of thyroid nodule type by statistical methods (SCAD and ANN methods) compared with the results of FNA testing revealed that the statistical modeling

  16. Model-Based Safety Analysis

    NASA Technical Reports Server (NTRS)

    Joshi, Anjali; Heimdahl, Mats P. E.; Miller, Steven P.; Whalen, Mike W.

    2006-01-01

    System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical.

  17. Quantum dot-based nanosensors for diagnosis via enzyme activity measurement.

    PubMed

    Knudsen, Birgitta R; Jepsen, Morten Leth; Ho, Yi-Ping

    2013-05-01

    Enzymes are essential in the human body, and the disorder of enzymatic activities has been associated with many different diseases and stages of disease. Luminescent semiconductor nanocrystals, also known as quantum dots (QDs), have garnered great attention in molecular diagnostics. Owing to their superior optical properties, tunable and narrow emissions, stable brightness and long lifetime, QD-based enzyme activity measurement has demonstrated improved detection sensitivity, which is considered particularly valuable for early disease diagnosis. Recent studies have also shown that QD-based nanosensors are capable of probing multiple enzyme activities simultaneously. This review highlights the current development of QD-based nanosensors for enzyme detection. The enzyme-QD hybrid system, equipped with unique electronic, optical and catalytic properties, is envisioned as a potential solution in addressing challenges in diagnostics and therapeutics.

  18. Pompe Disease: Diagnosis and Management. Evidence-Based Guidelines from a Canadian Expert Panel.

    PubMed

    Tarnopolsky, Mark; Katzberg, Hans; Petrof, Basil J; Sirrs, Sandra; Sarnat, Harvey B; Myers, Kimberley; Dupré, Nicolas; Dodig, Dubravka; Genge, Angela; Venance, Shannon L; Korngut, Lawrence; Raiman, Julian; Khan, Aneal

    2016-07-01

    Pompe disease is a lysosomal storage disorder caused by a deficiency of the enzyme acid alpha-glucosidase. Patients have skeletal muscle and respiratory weakness with or without cardiomyopathy. The objective of our review was to systematically evaluate the quality of evidence from the literature to formulate evidence-based guidelines for the diagnosis and management of patients with Pompe disease. The literature review was conducted using published literature, clinical trials, cohort studies and systematic reviews. Cardinal treatment decisions produced seven management guidelines and were assigned a GRADE classification based on the quality of evidence in the published literature. In addition, six recommendations were made based on best clinical practices but with insufficient data to form a guideline. Studying outcomes in rare diseases is challenging due to the small number of patients, but this is in particular the reason why we believe that informed treatment decisions need to consider the quality of the evidence. PMID:27055517

  19. Constraint Based Modeling Going Multicellular

    PubMed Central

    Martins Conde, Patricia do Rosario; Sauter, Thomas; Pfau, Thomas

    2016-01-01

    Constraint based modeling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modeling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multi-tissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem, or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches. PMID:26904548

  20. Constraint Based Modeling Going Multicellular.

    PubMed

    Martins Conde, Patricia do Rosario; Sauter, Thomas; Pfau, Thomas

    2016-01-01

    Constraint based modeling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modeling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multi-tissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem, or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches.

  1. Constraint Based Modeling Going Multicellular.

    PubMed

    Martins Conde, Patricia do Rosario; Sauter, Thomas; Pfau, Thomas

    2016-01-01

    Constraint based modeling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modeling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multi-tissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem, or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches. PMID:26904548

  2. Development and Evaluation of Reference Standards for Image-based Telemedicine Diagnosis and Clinical Research Studies in Ophthalmology

    PubMed Central

    Ryan, Michael C.; Ostmo, Susan; Jonas, Karyn; Berrocal, Audina; Drenser, Kimberly; Horowitz, Jason; Lee, Thomas C.; Simmons, Charles; Martinez-Castellanos, Maria-Ana; Chan, R.V. Paul; Chiang, Michael F.

    2014-01-01

    Information systems managing image-based data for telemedicine or clinical research applications require a reference standard representing the correct diagnosis. Accurate reference standards are difficult to establish because of imperfect agreement among physicians, and discrepancies between clinical vs. image-based diagnosis. This study is designed to describe the development and evaluation of reference standards for image-based diagnosis, which combine diagnostic impressions of multiple image readers with the actual clinical diagnoses. We show that agreement between image reading and clinical examinations was imperfect (689 [32%] discrepancies in 2148 image readings), as was inter-reader agreement (kappa 0.490-0.652). This was improved by establishing an image-based reference standard defined as the majority diagnosis given by three readers (13% discrepancies with image readers). It was further improved by establishing an overall reference standard that incorporated the clinical diagnosis (10% discrepancies with image readers). These principles of establishing reference standards may be applied to improve robustness of real-world systems supporting image-based diagnosis. PMID:25954463

  3. Development and Evaluation of Reference Standards for Image-based Telemedicine Diagnosis and Clinical Research Studies in Ophthalmology.

    PubMed

    Ryan, Michael C; Ostmo, Susan; Jonas, Karyn; Berrocal, Audina; Drenser, Kimberly; Horowitz, Jason; Lee, Thomas C; Simmons, Charles; Martinez-Castellanos, Maria-Ana; Chan, R V Paul; Chiang, Michael F

    2014-01-01

    Information systems managing image-based data for telemedicine or clinical research applications require a reference standard representing the correct diagnosis. Accurate reference standards are difficult to establish because of imperfect agreement among physicians, and discrepancies between clinical vs. image-based diagnosis. This study is designed to describe the development and evaluation of reference standards for image-based diagnosis, which combine diagnostic impressions of multiple image readers with the actual clinical diagnoses. We show that agreement between image reading and clinical examinations was imperfect (689 [32%] discrepancies in 2148 image readings), as was inter-reader agreement (kappa 0.490-0.652). This was improved by establishing an image-based reference standard defined as the majority diagnosis given by three readers (13% discrepancies with image readers). It was further improved by establishing an overall reference standard that incorporated the clinical diagnosis (10% discrepancies with image readers). These principles of establishing reference standards may be applied to improve robustness of real-world systems supporting image-based diagnosis.

  4. Portable optical fiber probe-based spectroscopic scanner for rapid cancer diagnosis: a new tool for intraoperative margin assessment.

    PubMed

    Lue, Niyom; Kang, Jeon Woong; Yu, Chung-Chieh; Barman, Ishan; Dingari, Narahara Chari; Feld, Michael S; Dasari, Ramachandra R; Fitzmaurice, Maryann

    2012-01-01

    There continues to be a significant clinical need for rapid and reliable intraoperative margin assessment during cancer surgery. Here we describe a portable, quantitative, optical fiber probe-based, spectroscopic tissue scanner designed for intraoperative diagnostic imaging of surgical margins, which we tested in a proof of concept study in human tissue for breast cancer diagnosis. The tissue scanner combines both diffuse reflectance spectroscopy (DRS) and intrinsic fluorescence spectroscopy (IFS), and has hyperspectral imaging capability, acquiring full DRS and IFS spectra for each scanned image pixel. Modeling of the DRS and IFS spectra yields quantitative parameters that reflect the metabolic, biochemical and morphological state of tissue, which are translated into disease diagnosis. The tissue scanner has high spatial resolution (0.25 mm) over a wide field of view (10 cm × 10 cm), and both high spectral resolution (2 nm) and high spectral contrast, readily distinguishing tissues with widely varying optical properties (bone, skeletal muscle, fat and connective tissue). Tissue-simulating phantom experiments confirm that the tissue scanner can quantitatively measure spectral parameters, such as hemoglobin concentration, in a physiologically relevant range with a high degree of accuracy (<5% error). Finally, studies using human breast tissues showed that the tissue scanner can detect small foci of breast cancer in a background of normal breast tissue. This tissue scanner is simpler in design, images a larger field of view at higher resolution and provides a more physically meaningful tissue diagnosis than other spectroscopic imaging systems currently reported in literatures. We believe this spectroscopic tissue scanner can provide real-time, comprehensive diagnostic imaging of surgical margins in excised tissues, overcoming the sampling limitation in current histopathology margin assessment. As such it is a significant step in the development of a platform

  5. Diagnosis and treatment of melanoma. European consensus-based interdisciplinary guideline--Update 2012.

    PubMed

    Garbe, Claus; Peris, Ketty; Hauschild, Axel; Saiag, Philippe; Middleton, Mark; Spatz, Alan; Grob, Jean-Jacques; Malvehy, Josep; Newton-Bishop, Julia; Stratigos, Alexander; Pehamberger, Hubert; Eggermont, Alexander M

    2012-10-01

    Cutaneous melanoma (CM) is potentially the most dangerous form of skin tumour and causes 90% of skin cancer mortality. A unique collaboration of multi-disciplinary experts from the European Dermatology Forum (EDF), the European Association of Dermato-Oncology (EADO) and the European Organization of Research and Treatment of Cancer (EORTC) was formed to make recommendations on CM diagnosis and treatment, based on systematic literature reviews and the experts' experience. Diagnosis is made clinically and staging is based upon the AJCC system. CMs are excised with one to two centimetre safety margins. Sentinel lymph node dissection (SLND) is routinely offered as a staging procedure in patients with tumours more than 1mm in thickness, although there is as yet no clear survival benefit for this approach. Interferon-α treatment may be offered to patients with stage II and III melanoma as an adjuvant therapy, as this treatment increases at least the disease-free survival (DFS) and less clear the overall survival (OS) time. The treatment is however associated with significant toxicity. In distant metastasis, all options of surgical therapy have to be considered thoroughly. In the absence of surgical options, systemic treatment is indicated. BRAF inhibitors like vemurafenib for BRAF mutated patients as well as the CTLA-4 antibody ipilimumab offer new therapeutic opportunities apart from conventional chemotherapy. Therapeutic decisions in stage IV patients should be primarily made by an interdisciplinary oncology team ('tumour board'). PMID:22981501

  6. Optical and dielectric sensors based on antimicrobial peptides for microorganism diagnosis

    PubMed Central

    Silva, Rafael R.; Avelino, Karen Y. P. S.; Ribeiro, Kalline L.; Franco, Octavio L.; Oliveira, Maria D. L.; Andrade, Cesar A. S.

    2014-01-01

    Antimicrobial peptides (AMPs) are natural compounds isolated from a wide variety of organisms that include microorganisms, insects, amphibians, plants, and humans. These biomolecules are considered as part of the innate immune system and are known as natural antibiotics, presenting a broad spectrum of activities against bacteria, fungi, and/or viruses. Technological innovations have enabled AMPs to be utilized for the development of novel biodetection devices. Advances in nanotechnology, such as the synthesis of nanocomposites, nanoparticles, and nanotubes have permitted the development of nanostructured platforms with biocompatibility and greater surface areas for the immobilization of biocomponents, arising as additional tools for obtaining more efficient biosensors. Diverse AMPs have been used as biological recognition elements for obtaining biosensors with more specificity and lower detection limits, whose analytical response can be evaluated through electrochemical impedance and fluorescence spectroscopies. AMP-based biosensors have shown potential for applications such as supplementary tools for conventional diagnosis methods of microorganisms. In this review, conventional methods for microorganism diagnosis as well new strategies using AMPs for the development of impedimetric and fluorescent biosensors are highlighted. AMP-based biosensors show promise as methods for diagnosing infections and bacterial contaminations as well as applications in quality control for clinical analyses and microbiological laboratories. PMID:25191319

  7. Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis

    PubMed Central

    Zhu, Xiaofeng; Suk, Heung-Il; Shen, Dinggang

    2015-01-01

    Recent studies on Alzheimer's Disease (AD) or its prodromal stage, Mild Cognitive Impairment (MCI), diagnosis presented that the tasks of identifying brain disease status and predicting clinical scores based on neuroimaging features were highly related to each other. However, these tasks were often conducted independently in the previous studies. Regarding the feature selection, to our best knowledge, most of the previous work considered a loss function defined as an element-wise difference between the target values and the predicted ones. In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix. The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i.e., clinical scores prediction and disease status identification. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods. PMID:26379415

  8. Evidence-based consensus on the diagnosis, prevention and management of hepatitis C virus disease

    PubMed Central

    Shaheen, Mahrukh Akbar; Idrees, Muhammad

    2015-01-01

    Hepatitis C virus (HCV) is a potent human pathogen and is one of the main causes of chronic hepatitis round the world. The present review describes the evidence-based consensus on the diagnosis, prevention and management of HCV disease. Various techniques, for the detection of anti-HCV immunoglobulin G immunoassays, detection of HCV RNA by identifying virus-specific molecules nucleic acid testings, recognition of core antigen for diagnosis of HCV, quantitative antigen assay, have been used to detect HCV RNA and core antigen. Advanced technologies such as nanoparticle-based diagnostic assays, loop-mediated isothermal amplification and aptamers and Ortho trak-C assay have also come to the front that provides best detection results with greater ease and specificity for detection of HCV. It is of immense importance to prevent this infection especially among the sexual partners, injecting drug users, mother-to-infant transmission of HCV, household contact, healthcare workers and people who get tattoos and piercing on their skin. Management of this infection is intended to eradicate it out of the body of patients. Management includes examining the treatment (efficacy and protection), assessment of hepatic condition before commencing therapy, controlling the parameters upon which dual and triple therapies work, monitoring the body after treatment and adjusting the co-factors. Examining the treatment in some special groups of people (HIV/HCV co-infected, hemodialysis patients, renal transplanted patients). PMID:25848486

  9. ALA-based fluorescent diagnosis of malignant oral lesions in the presence of bacterial porphyrin formation

    NASA Astrophysics Data System (ADS)

    Schleier, P.; Berndt, A.; Zinner, K.; Zenk, W.; Dietel, W.; Pfister, W.

    2006-02-01

    The aminolevulinic acid (5-ALA) -based fluorescence diagnosis has been found to be promising for an early detection and demarcation of superficial oral squamous cell carcinomas (OSCC). This method has previously demonstrated high sensitivity, however this clinical trial showed a specificity of approximately 62 %. This specificity was mainly restricted by tumor detection in the oral cavity in the presence of bacteria. After topical ALA application in the mouth of patients with previously diagnosed OSSC, red fluorescent areas were observed which did not correlate to confirm histological findings. Swabs and plaque samples were taken from 44 patients and cultivated microbiologically. Fluorescence was investigated (OMA-system) from 32 different bacteria strains found naturally in the oral cavity. After ALA incubation, 30 of 32 strains were found to synthesize fluorescent porphyrins, mainly Protoporphyrin IX. Also multiple fluorescent spectra were obtained having peak wavelengths of 636 nm and around 618 nm - 620 nm indicating synthesis of different porphyrins, such as the lipophylic Protoporphyrin IX (PpIX) and hydrophylic porphyrins (water soluble porphyrins, wsp). Of the 32 fluorescent bacterial strains, 18 produced wsp, often in combination with PpIX, and 5 produced solely wsp. These results clarify that ALA-based fluorescence diagnosis without consideration or suppression of bacteria fluorescence may lead to false-positive findings. It is necessary to suppress bacteria fluorescence with suitable antiseptics before starting the procedure. In this study, when specific antiseptic pre-treatment was performed bacterial associated fluorescence was significantly reduced.

  10. Optical and dielectric sensors based on antimicrobial peptides for microorganism diagnosis.

    PubMed

    Silva, Rafael R; Avelino, Karen Y P S; Ribeiro, Kalline L; Franco, Octavio L; Oliveira, Maria D L; Andrade, Cesar A S

    2014-01-01

    Antimicrobial peptides (AMPs) are natural compounds isolated from a wide variety of organisms that include microorganisms, insects, amphibians, plants, and humans. These biomolecules are considered as part of the innate immune system and are known as natural antibiotics, presenting a broad spectrum of activities against bacteria, fungi, and/or viruses. Technological innovations have enabled AMPs to be utilized for the development of novel biodetection devices. Advances in nanotechnology, such as the synthesis of nanocomposites, nanoparticles, and nanotubes have permitted the development of nanostructured platforms with biocompatibility and greater surface areas for the immobilization of biocomponents, arising as additional tools for obtaining more efficient biosensors. Diverse AMPs have been used as biological recognition elements for obtaining biosensors with more specificity and lower detection limits, whose analytical response can be evaluated through electrochemical impedance and fluorescence spectroscopies. AMP-based biosensors have shown potential for applications such as supplementary tools for conventional diagnosis methods of microorganisms. In this review, conventional methods for microorganism diagnosis as well new strategies using AMPs for the development of impedimetric and fluorescent biosensors are highlighted. AMP-based biosensors show promise as methods for diagnosing infections and bacterial contaminations as well as applications in quality control for clinical analyses and microbiological laboratories.

  11. Towards the Neuropsychological Diagnosis of Alzheimer's Disease: A Hybrid Model in Decision Making

    NASA Astrophysics Data System (ADS)

    de Castro, Ana Karoline Araujo; Pinheiro, Placido Rogerio; Pinheiro, Mirian Caliope Dantas

    Dementias are syndromes described by a decline in memory and other neuropsychological changes especially occurring in the elderly and increasing exponentially in function of age. Due to this fact and the therapeutical limitations in the most advanced stage of the disease, diagnosis of Alzheimer's disease is extremely important and it can provide better life conditions to patients and their families. This work presents a hybrid model, combining Influence Diagrams and the Multicriteria Method, for aiding to discover, from a battery of tests, which are the most attractive questions, in relation to the stages of CDR (Clinical Dementia Rating) in decision making for the diagnosis of Alzheimer's disease. This disease is the most common dementia. Influence Diagram is implemented using GeNie tool. Next, the judgment matrixes are constructed to obtain cardinal value scales which are implemented through MACBETH Multicriteria Methodology. The modeling and evaluation processes were carried out through a battery of standardized assessments for the evaluation of cases with Alzheimer's disease developed by Consortium to Establish a Registry for Alzheimer's disease (CERAD).

  12. Preclinical photoacoustic models: application for ultrasensitive single cell malaria diagnosis in large vein and artery

    PubMed Central

    Menyaev, Yulian A.; Carey, Kai A.; Nedosekin, Dmitry A.; Sarimollaoglu, Mustafa; Galanzha, Ekaterina I.; Stumhofer, Jason S.; Zharov, Vladimir P.

    2016-01-01

    In vivo photoacoustic flow cytometry (PAFC) has demonstrated potential for early diagnosis of deadly diseases through detection of rare circulating tumor cells, pathogens, and clots in nearly the entire blood volume. Before clinical application, this promising diagnostic platform requires verification and optimization using adequate preclinical models. We show here that this can be addressed by examination of large mouse blood vessels which are similar in size, depth and flow velocity to human vessels used in PAFC. Using this model, we verified the capability of PAFC for ultrasensitive, noninvasive, label-free, rapid malaria diagnosis. The time-resolved detection of delayed PA signals from deep vessels provided complete elimination of background from strongly pigmented skin. We discovered that PAFC’s sensitivity is higher during examination of infected cells in arteries compared to veins at similar flow rate. Our advanced PAFC platform integrating a 1060 nm laser with tunable pulse rate and width, a wearable probe with a focused transducer, and linear and nonlinear nanobubble-amplified signal processing demonstrated detection of parasitemia at the unprecedented level of 0.00000001% within 20 seconds and the potential to further improve the sensitivity 100-fold in humans, that is approximately 106 times better than in existing malaria tests. PMID:27699126

  13. Quantitative Diagnosis of Tongue Cancer from Histological Images in an Animal Model

    PubMed Central

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo Georgia; Fei, Baowei

    2016-01-01

    We developed a chemically-induced oral cancer animal model and a computer aided method for tongue cancer diagnosis. The animal model allows us to monitor the progress of the lesions over time. Tongue tissue dissected from mice was sent for histological processing. Representative areas of hematoxylin and eosin (H&E) stained tissue from tongue sections were captured for classifying tumor and non-tumor tissue. The image set used in this paper consisted of 214 color images (114 tumor and 100 normal tissue samples). A total of 738 color, texture, morphometry and topology features were extracted from the histological images. The combination of image features from epithelium tissue and its constituent nuclei and cytoplasm has been demonstrated to improve the classification results. With ten iteration nested cross validation, the method achieved an average sensitivity of 96.5% and a specificity of 99% for tongue cancer detection. The next step of this research is to apply this approach to human tissue for computer aided diagnosis of tongue cancer. PMID:27656036

  14. Preclinical photoacoustic models: application for ultrasensitive single cell malaria diagnosis in large vein and artery

    PubMed Central

    Menyaev, Yulian A.; Carey, Kai A.; Nedosekin, Dmitry A.; Sarimollaoglu, Mustafa; Galanzha, Ekaterina I.; Stumhofer, Jason S.; Zharov, Vladimir P.

    2016-01-01

    In vivo photoacoustic flow cytometry (PAFC) has demonstrated potential for early diagnosis of deadly diseases through detection of rare circulating tumor cells, pathogens, and clots in nearly the entire blood volume. Before clinical application, this promising diagnostic platform requires verification and optimization using adequate preclinical models. We show here that this can be addressed by examination of large mouse blood vessels which are similar in size, depth and flow velocity to human vessels used in PAFC. Using this model, we verified the capability of PAFC for ultrasensitive, noninvasive, label-free, rapid malaria diagnosis. The time-resolved detection of delayed PA signals from deep vessels provided complete elimination of background from strongly pigmented skin. We discovered that PAFC’s sensitivity is higher during examination of infected cells in arteries compared to veins at similar flow rate. Our advanced PAFC platform integrating a 1060 nm laser with tunable pulse rate and width, a wearable probe with a focused transducer, and linear and nonlinear nanobubble-amplified signal processing demonstrated detection of parasitemia at the unprecedented level of 0.00000001% within 20 seconds and the potential to further improve the sensitivity 100-fold in humans, that is approximately 106 times better than in existing malaria tests.

  15. Detection and Modeling of High-Dimensional Thresholds for Fault Detection and Diagnosis

    NASA Technical Reports Server (NTRS)

    He, Yuning

    2015-01-01

    Many Fault Detection and Diagnosis (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like oil pressure too high, analog sensor values need to be discretized using a suitablethreshold. Time series of analog and discrete sensor readings are processed and discretized as they come in. This task isusually performed by the wrapper code'' of the FDD system, together with signal preprocessing and filtering. In practice,selecting the right threshold is very difficult, because it heavily influences the quality of diagnosis. If a threshold causesthe alarm trigger even in nominal situations, false alarms will be the consequence. On the other hand, if threshold settingdoes not trigger in case of an off-nominal condition, important alarms might be missed, potentially causing hazardoussituations. In this paper, we will in detail describe the underlying statistical modeling techniques and algorithm as well as the Bayesian method for selecting the most likely shape and its parameters. Our approach will be illustrated by several examples from the Aerospace domain.

  16. Quantitative diagnosis of tongue cancer from histological images in an animal model

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo G.; Fei, Baowei

    2016-03-01

    We developed a chemically-induced oral cancer animal model and a computer aided method for tongue cancer diagnosis. The animal model allows us to monitor the progress of the lesions over time. Tongue tissue dissected from mice was sent for histological processing. Representative areas of hematoxylin and eosin stained tissue from tongue sections were captured for classifying tumor and non-tumor tissue. The image set used in this paper consisted of 214 color images (114 tumor and 100 normal tissue samples). A total of 738 color, texture, morphometry and topology features were extracted from the histological images. The combination of image features from epithelium tissue and its constituent nuclei and cytoplasm has been demonstrated to improve the classification results. With ten iteration nested cross validation, the method achieved an average sensitivity of 96.5% and a specificity of 99% for tongue cancer detection. The next step of this research is to apply this approach to human tissue for computer aided diagnosis of tongue cancer.

  17. Two Stage Helical Gearbox Fault Detection and Diagnosis based on Continuous Wavelet Transformation of Time Synchronous Averaged Vibration Signals

    NASA Astrophysics Data System (ADS)

    Elbarghathi, F.; Wang, T.; Zhen, D.; Gu, F.; Ball, A.

    2012-05-01

    Vibration signals from a gearbox are usually very noisy which makes it difficult to find reliable symptoms of a fault in a multistage gearbox. This paper explores the use of time synchronous average (TSA) to suppress the noise and Continue Wavelet Transformation (CWT) to enhance the non-stationary nature of fault signal for more accurate fault diagnosis. The results obtained in diagnosis an incipient gear breakage show that fault diagnosis results can be improved by using an appropriate wavelet. Moreover, a new scheme based on the level of wavelet coefficient amplitudes of baseline data alone, without faulty data samples, is suggested to select an optimal wavelet.

  18. Space-Based Diagnosis of Surface Ozone Sensitivity to Anthropogenic Emissions

    NASA Technical Reports Server (NTRS)

    Martin, Randall V.; Fiore, Arlene M.; VanDonkelaar, Aaron

    2004-01-01

    We present a novel capability in satellite remote sensing with implications for air pollution control strategy. We show that the ratio of formaldehyde columns to tropospheric nitrogen dioxide columns is an indicator of the relative sensitivity of surface ozone to emissions of nitrogen oxides (NO(x) = NO + NO2) and volatile organic compounds (VOCs). The diagnosis from these space-based observations is highly consistent with current understanding of surface ozone chemistry based on in situ observations. The satellite-derived ratios indicate that surface ozone is more sensitive to emissions of NO(x) than of VOCs throughout most continental regions of the Northern Hemisphere during summer. Exceptions include Los Angeles and industrial areas of Germany. A seasonal transition occurs in the fall when surface ozone becomes less sensitive to NOx and more sensitive to VOCs.

  19. Current Advances in Polymer-Based Nanotheranostics for Cancer Treatment and Diagnosis

    PubMed Central

    2015-01-01

    Nanotheranostics is a relatively new, fast-growing field that combines the advantages of treatment and diagnosis via a single nanoscale carrier. The ability to bundle both therapeutic and diagnostic capabilities into one package offers exciting prospects for the development of novel nanomedicine. Nanotheranostics can deliver treatment while simultaneously monitoring therapy response in real-time, thereby decreasing the potential of over- or under-dosing patients. Polymer-based nanomaterials, in particular, have been used extensively as carriers for both therapeutic and bioimaging agents and thus hold great promise for the construction of multifunctional theranostic formulations. Herein, we review recent advances in polymer-based systems for nanotheranostics, with a particular focus on their applications in cancer research. We summarize the use of polymer nanomaterials for drug delivery, gene delivery, and photodynamic therapy, combined with imaging agents for magnetic resonance imaging, radionuclide imaging, and fluorescence imaging. PMID:25014486

  20. Irritable bowel syndrome: Pathogenesis, diagnosis, treatment, and evidence-based medicine

    PubMed Central

    Saha, Lekha

    2014-01-01

    Irritable bowel syndrome (IBS) is a chronic and debilitating functional gastrointestinal disorder that affects 9%-23% of the population across the world. The percentage of patients seeking health care related to IBS approaches 12% in primary care practices and is by far the largest subgroup seen in gastroenterology clinics. It has been well documented that these patients exhibit a poorer quality of life and utilize the health care system to a greater degree than patients without this diagnosis. The pathophysiology of IBS is not clear. Many theories have been put forward, but the exact cause of IBS is still uncertain. According to the updated ROME III criteria, IBS is a clinical diagnosis and presents as one of the three predominant subtypes: (1) IBS with constipation (IBS-C); (2) IBS with diarrhea (IBS-D); and (3) mixed IBS (IBS-M); former ROME definitions refer to IBS-M as alternating IBS (IBS-A). Across the IBS subtypes, the presentation of symptoms may vary among patients and change over time. Patients report the most distressing symptoms to be abdominal pain, straining, myalgias, urgency, bloating and feelings of serious illness. The complexity and diversity of IBS presentation makes treatment difficult. Although there are reviews and guidelines for treating IBS, they focus on the efficacy of medications for IBS symptoms using high-priority endpoints, leaving those of lower priority largely unreported. Therefore, the aim of this review is to provide a comprehensive evidence-based review of the diagnosis, pathogenesis and treatment to guide clinicians diagnosing and treating their patients. PMID:24944467

  1. Diagnosis of alpha-1 antitrypsin deficiency: a population-based study

    PubMed Central

    Barrecheguren, Miriam; Monteagudo, Mónica; Simonet, Pere; Llor, Carl; Rodriguez, Esther; Ferrer, Jaume; Esquinas, Cristina; Miravitlles, Marc

    2016-01-01

    Introduction Alpha-1 antitrypsin deficiency (AATD) remains an underdiagnosed condition despite initiatives developed to increase awareness. The objective was to describe the current situation of the diagnosis of AATD in primary care (PC) in Catalonia, Spain. Methods We performed a population-based study with data from the Information System for Development in Research in Primary Care, a population database that contains information of 5.8 million inhabitants (80% of the population of Catalonia). We collected the number of alpha-1 antitrypsin (AAT) determinations performed in the PC in two periods (2007–2008 and 2010–2011) and described the characteristics of the individuals tested. Results A total of 12,409 AAT determinations were performed (5,559 in 2007–2008 and 6,850 in 2010–2011), with 10.7% of them in children. As a possible indication for AAT determination, 28.9% adults and 29.4% children had a previous diagnosis of a disease related to AATD; transaminase levels were above normal in 17.7% of children and 47.1% of adults. In total, 663 (5.3%) individuals had intermediate AATD (50–100 mg/dL), 24 (0.2%) individuals had a severe deficiency (<50 mg/dL), with a prevalence of 0.19 cases of severe deficiency per 100 determinations. Nine (41%) of the adults with severe deficiency had a previous diagnosis of COPD/emphysema, and four (16.7%) were diagnosed with COPD within 6 months. Conclusion The number of AAT determinations in the PC is low in relation to the prevalence of COPD but increased slightly along the study period. The indication to perform the test is not always clear, and patients detected with deficiency are not always referred to a specialist. PMID:27274221

  2. An Economic Evaluation of Home Versus Laboratory-Based Diagnosis of Obstructive Sleep Apnea

    PubMed Central

    Kim, Richard D.; Kapur, Vishesh K.; Redline-Bruch, Julie; Rueschman, Michael; Auckley, Dennis H.; Benca, Ruth M.; Foldvary-Schafer, Nancy R.; Iber, Conrad; Zee, Phyllis C.; Rosen, Carol L.; Redline, Susan; Ramsey, Scott D.

    2015-01-01

    Study Objectives: We conducted an economic analysis of the HomePAP study, a multicenter randomized clinical trial that compared home-based versus laboratory-based testing for the diagnosis and management of obstructive sleep apnea (OSA). Design: A cost-minimization analysis from the payer and provider perspectives was performed, given that 3-mo clinical outcomes were equivalent. Setting: Seven academic sleep centers. Participants: There were 373 subjects at high risk for moderate to severe OSA. Interventions: Subjects were randomized to either home-based limited channel portable monitoring followed by unattended autotitration with continuous positive airway pressure (CPAP), versus a traditional pathway of in-laboratory sleep study and CPAP titration. Measurements and Results: From the payer perspective, per subject costs for the laboratory-based pathway were $1,840 (95% confidence interval [CI] $1,660, $2,015) compared to $1,575 (95% CI $1,439, $1,716) for the home-based pathway under the base case. Costs were $264 (95% CI $39, $496, P = 0.02) in favor of the home arm. From the provider perspective, per subject costs for the laboratory arm were $1,697 (95% CI $1,566, $1,826) compared to $1,736 (95% CI $1,621, $1,857) in the home arm, for a difference of $40 (95% CI −$213, $142, P = 0.66) in favor of the laboratory arm under the base case. The provider operating margin was $142 (95% CI $85, $202,P < 0.01) in the laboratory arm, compared to a loss of −$161 (95% CI −$202, −$120, P < 0.01) in the home arm. Conclusions: For payers, a home-based diagnostic pathway for obstructive sleep apnea with robust patient support incurs fewer costs than a laboratory-based pathway. For providers, costs are comparable if not higher, resulting in a negative operating margin. Clinicaltrials.gov Identifier: NCT00642486. Citation: Kim RD, Kapur VK, Redline-Bruch J, Rueschman M, Auckley DH, Benca RM, Foldvary-Schafer NR, Iber C, Zee PC, Rosen CL, Redline S, Ramsey SD. An economic

  3. Matrix Failure Modes and Effects Analysis as a Knowledge Base for a Real Time Automated Diagnosis Expert System

    NASA Technical Reports Server (NTRS)

    Herrin, Stephanie; Iverson, David; Spukovska, Lilly; Souza, Kenneth A. (Technical Monitor)

    1994-01-01

    Failure Modes and Effects Analysis contain a wealth of information that can be used to create the knowledge base required for building automated diagnostic Expert systems. A real time monitoring and diagnosis expert system based on an actual NASA project's matrix failure modes and effects analysis was developed. This Expert system Was developed at NASA Ames Research Center. This system was first used as a case study to monitor the Research Animal Holding Facility (RAHF), a Space Shuttle payload that is used to house and monitor animals in orbit so the effects of space flight and microgravity can be studied. The techniques developed for the RAHF monitoring and diagnosis Expert system are general enough to be used for monitoring and diagnosis of a variety of other systems that undergo a Matrix FMEA. This automated diagnosis system was successfully used on-line and validated on the Space Shuttle flight STS-58, mission SLS-2 in October 1993.

  4. Model-based machine learning.

    PubMed

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

  5. Model-based machine learning.

    PubMed

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

  6. a Historical Timber Frame Model for Diagnosis and Documentation Before Building Restoration

    NASA Astrophysics Data System (ADS)

    Koehl, M.; Viale, A.; Reeb, S.

    2013-09-01

    The aim of the project that is described in this paper was to define a four-level timber frame survey mode of a historical building: the so-called "Andlau's Seigniory", Alsace, France. This historical building (domain) was built in the late XVIth century and is now in a stage of renovation in order to become a heritage interpretation centre. The used measurement methods combine Total Station measurements, Photogrammetry and 3D Terrestrial Laser scanner. Different modelling workflows were tested and compared according to the data acquisition method, but also according to the characteristics of the reconstructed model in terms of accuracy and level of detail. 3D geometric modelling of the entire structure was performed including modelling the degree of detail adapted to the needs. The described 3D timber framework exists now in different versions, from a theoretical and geometrical one up to a very detailed one, in which measurements and evaluation of deformation by time are potentially allowed. The virtually generated models involving archaeologists, architects, historians and specialists in historical crafts, are intended to be used during the four stages of the project: (i) knowledge of the current state of needs for diagnosis and understanding of former construction techniques; (ii) preparation and evaluation of restoration steps; (iii) knowledge and documentation concerning the archaeological object; (iv) transmission and dissemination of knowledge through the implementation of museum animations. Among the generated models we can also find a documentation of the site in the form of virtual tours created from panoramic photographs before and during the restoration works. Finally, the timber framework model was structured and integrated into a 3D GIS, where the association of descriptive and complementary digital documents was possible. Both offer tools leading to the diagnosis, the understanding of the structure, knowledge dissemination, documentation and the

  7. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram

    NASA Astrophysics Data System (ADS)

    Zhang, Yongxiang; Randall, R. B.

    2009-07-01

    The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. However, much practical diagnostic equipment for carrying out the analysis gives little flexibility to change the analysis parameters for different working conditions, such as variation in rotating speed and different fault types. Because the signals from a flawed bearing have features of non-stationarity, wide frequency range and weak strength, it can be very difficult to choose the best analysis parameters for diagnosis. However, the kurtosis of the vibration signals of a bearing is different from normal to bad condition, and is robust in varying conditions. The fast kurtogram gives rough analysis parameters very efficiently, but filter centre frequency and bandwidth cannot be chosen entirely independently. Genetic algorithms have a strong ability for optimization, but are slow unless initial parameters are close to optimal. Therefore, the authors present a model and algorithm to design the parameters for optimal resonance demodulation using the combination of fast kurtogram for initial estimates, and a genetic algorithm for final optimization. The feasibility and the effectiveness of the proposed method are demonstrated by experiment and give better results than the classical method of arbitrarily choosing a resonance to demodulate. The method gives more flexibility in choosing optimal parameters than the fast kurtogram alone.

  8. Forensic diagnosis of ante- and postmortem burn based on aquaporin-3 gene expression in the skin.

    PubMed

    Kubo, Hidemichi; Hayashi, Takahito; Ago, Kazutoshi; Ago, Mihoko; Kanekura, Takuro; Ogata, Mamoru

    2014-05-01

    In order to diagnose death associated with fire, it is essential to show that the person was exposed to heat while still alive. We investigated both AQP1 and AQP3 expression in the skin of an experimental burn model, as well as in forensic autopsy cases, and discuss its role in the differential diagnosis of ante- and postmortem burns. In animal experiments, there was no difference in AQP1 gene expression among four groups (n=4): antemortem burn, postmortem burn, mechanical wound, and control. However, AQP3 expression in the antemortem burn was increased significantly compared with that of the other groups even at 5min after burn. Water content of the skin was decreased significantly by the burn procedure. Consistent with animal experiments, AQP3 gene expression in the skin of antemortem burn cases was increased significantly compared with postmortem burns, mechanical wounds, and controls (n=12 in each group). These observations suggest that dermal AQP3 gene expression was increased to maintain water homeostasis in response to dehydration from burn. Finally, our results suggest that AQP3 gene expression may be useful for forensic molecular diagnosis of antemortem burn.

  9. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis

    NASA Astrophysics Data System (ADS)

    He, Shuilong; Chen, Jinglong; Zhou, Zitong; Zi, Yanyang; Wang, Yanxue; Wang, Xiaodong

    2016-08-01

    Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what's more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.

  10. Irritable bowel syndrome in children: Pathogenesis, diagnosis and evidence-based treatment

    PubMed Central

    Sandhu, Bhupinder Kaur; Paul, Siba Prosad

    2014-01-01

    Irritable bowel syndrome (IBS) is the commonest cause of recurrent abdominal pain (RAP) in children in both more developed and developing parts of the world. It is defined by the Rome III criteria for functional gastrointestinal disorders. It is characterized by abdominal pain that is improved by defecation and whose onset is associated with a change in stool form and or frequency and is not explained by structural or biochemical abnormalities. It is estimated that 10%-15% of older children and adolescents suffer from IBS. IBS can be considered to be a brain-gut disorder possibly due to complex interaction between environmental and hereditary factors. The diagnosis of IBS is made based on the Rome III criteria together with ruling out organic causes of RAP in children such as inflammatory bowel disease and celiac disease. Once the diagnosis of IBS is made, it is important to explain to the parents (and children) that there is no serious underlying disease. This reassurance may be effective treatment in a large number of cases. Lifestyle modifications, stress management, dietary interventions and probiotics may be beneficial in some cases. Although there is limited evidence for efficacy of pharmacological therapies such as antispasmodics and antidiarrheals; these have a role in severe cases. Biopsychosocial therapies have shown encouraging results in initial trials but are beset by limited availability. Further research is necessary to understand the pathophysiology and provide specific focused therapies. PMID:24876724

  11. The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

    NASA Technical Reports Server (NTRS)

    Lee, S. C.

    1989-01-01

    The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.

  12. Efficient Targeted Next Generation Sequencing-Based Workflow for Differential Diagnosis of Alport-Related Disorders.

    PubMed

    Kovács, Gábor; Kalmár, Tibor; Endreffy, Emőke; Ondrik, Zoltán; Iványi, Béla; Rikker, Csaba; Haszon, Ibolya; Túri, Sándor; Sinkó, Mária; Bereczki, Csaba; Maróti, Zoltán

    2016-01-01

    Alport syndrome (AS) is an inherited type IV collagen nephropathies characterized by microscopic hematuria during early childhood, the development of proteinuria and progression to end-stage renal disease. Since choosing the right therapy, even before the onset of proteinuria, can delay the onset of end-stage renal failure and improve life expectancy, the earliest possible differential diagnosis is desired. Practically, this means the identification of mutation(s) in COL4A3-A4-A5 genes. We used an efficient, next generation sequencing based workflow for simultaneous analysis of all three COL4A genes in three individuals and fourteen families involved by AS or showing different level of Alport-related symptoms. We successfully identified mutations in all investigated cases, including 14 unpublished mutations in our Hungarian cohort. We present an easy to use unified clinical/diagnostic terminology and workflow not only for X-linked but for autosomal AS, but also for Alport-related diseases. In families where a diagnosis has been established by molecular genetic analysis, the renal biopsy may be rendered unnecessary. PMID:26934356

  13. Recent advances in diagnosis and treatment of gliomas using chlorotoxin-based bioconjugates.

    PubMed

    Cheng, Yongjun; Zhao, Jinhua; Qiao, Wenli; Chen, Kai

    2014-01-01

    Malignant gliomas, especially glioblastoma multiforme, are the most widely distributed and deadliest brain tumors because of their resistance to surgical and medical treatment. Research of glioma-specific bioconjugates for diagnosis and therapy developed rapidly during the past several years. Many studies have demonstrated that chlorotoxin (CTX) and Buthus martensii Karsch chlorotoxin (BmK CT) specifically inhibited glioma cells growth and metastasis, and accelerated tumor apoptosis. The bioconjugates of CTX or BmK CT with other molecules have played an increasing role in diagnostic imaging and treatment of gliomas. To date, CTX-based bioconjugates have achieved great success in phase I/II clinical trials about safety profiles. Here, we will provide a review on the important role of ion channels in the underlying mechanisms of gliomas invasive growth and how CTX suppresses gliomas proliferation and migration. We will summarize the recent advances in the applications of CTX bioconjugates for gliomas diagnosis and treatment. In addition, we will review recent studies on BmK CT bioconjugates and compare their efficacies with CTX derivatives. Finally, we will address advantages and challenges in the use of CTX or BmK CT bioconjugates as specific agents for theranostic applications in gliomas.

  14. Development of a Fluorescence-Based Sensor for Rapid Diagnosis of Cyanide Exposure

    PubMed Central

    2015-01-01

    Although commonly known as a highly toxic chemical, cyanide is also an essential reagent for many industrial processes in areas such as mining, electroplating, and synthetic fiber production. The “heavy” use of cyanide in these industries, along with its necessary transportation, increases the possibility of human exposure. Because the onset of cyanide toxicity is fast, a rapid, sensitive, and accurate method for the diagnosis of cyanide exposure is necessary. Therefore, a field sensor for the diagnosis of cyanide exposure was developed based on the reaction of naphthalene dialdehyde, taurine, and cyanide, yielding a fluorescent β-isoindole. An integrated cyanide capture “apparatus”, consisting of sample and cyanide capture chambers, allowed rapid separation of cyanide from blood samples. Rabbit whole blood was added to the sample chamber, acidified, and the HCN gas evolved was actively transferred through a stainless steel channel to the capture chamber containing a basic solution of naphthalene dialdehyde (NDA) and taurine. The overall analysis time (including the addition of the sample) was <3 min, the linear range was 3.13–200 μM, and the limit of detection was 0.78 μM. None of the potential interferents investigated (NaHS, NH4OH, NaSCN, and human serum albumin) produced a signal that could be interpreted as a false positive or a false negative for cyanide exposure. Most importantly, the sensor was 100% accurate in diagnosing cyanide poisoning for acutely exposed rabbits. PMID:24383576

  15. Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis

    NASA Astrophysics Data System (ADS)

    Liu, Hongmei; Wang, Xuan; Lu, Chen

    2015-08-01

    A rolling bearing vibration signal is nonlinear and non-stationary and has multiple components and multifractal properties. A rolling-bearing fault-diagnosis method based on Local Characteristic-scale Decomposition-Teager Energy Operator (LCD-TEO) and multifractal detrended fluctuation analysis (MF-DFA) is first proposed in this paper. First, the vibration signal was decomposed into several intrinsic scale components (ISCs) by using LCD, which is a newly developed signal decomposition method. Second, the instantaneous amplitude was obtained by applying the TEO to each major ISC for demodulation. Third, the intrinsic multifractality features hidden in each major ISC were extracted by using MF-DFA, among which the generalized Hurst exponents are selected as the multifractal feature in this paper. Finally, the feature vectors were obtained by applying principal components analysis (PCA) to the extracted multifractality features, thus reducing the dimension of the multifractal features and obtaining the fault feature insensitive to variation in working conditions, further enhancing the accuracy of diagnosis. According to the extracted feature vector, rolling bearing faults can be diagnosed under variable working conditions. The experimental results demonstrate its desirable diagnostic performance under both different working conditions and different fault severities. Simultaneously, the results of comparison show that the performance of the proposed diagnostic method outperforms that of Hilbert-Huang transform (HHT) combined with MF-DFA or LCD-TEO combined with mono-fractal analysis.

  16. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

    PubMed Central

    Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

    2015-01-01

    Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

  17. Efficient Targeted Next Generation Sequencing-Based Workflow for Differential Diagnosis of Alport-Related Disorders

    PubMed Central

    Endreffy, Emőke; Ondrik, Zoltán; Iványi, Béla; Rikker, Csaba; Haszon, Ibolya; Túri, Sándor; Sinkó, Mária; Bereczki, Csaba; Maróti, Zoltán

    2016-01-01

    Alport syndrome (AS) is an inherited type IV collagen nephropathies characterized by microscopic hematuria during early childhood, the development of proteinuria and progression to end-stage renal disease. Since choosing the right therapy, even before the onset of proteinuria, can delay the onset of end-stage renal failure and improve life expectancy, the earliest possible differential diagnosis is desired. Practically, this means the identification of mutation(s) in COL4A3-A4-A5 genes. We used an efficient, next generation sequencing based workflow for simultaneous analysis of all three COL4A genes in three individuals and fourteen families involved by AS or showing different level of Alport-related symptoms. We successfully identified mutations in all investigated cases, including 14 unpublished mutations in our Hungarian cohort. We present an easy to use unified clinical/diagnostic terminology and workflow not only for X-linked but for autosomal AS, but also for Alport-related diseases. In families where a diagnosis has been established by molecular genetic analysis, the renal biopsy may be rendered unnecessary. PMID:26934356

  18. Evaluation and diagnosis of wrist pain: a case-based approach.

    PubMed

    Shehab, Ramsey; Mirabelli, Mark H

    2013-04-15

    Patients with wrist pain commonly present with an acute injury or spontaneous onset of pain without a definite traumatic event. A fall onto an outstretched hand can lead to a scaphoid fracture, which is the most commonly fractured carpal bone. Conventional radiography alone can miss up to 30 percent of scaphoid fractures. Specialized views (e.g., posteroanterior in ulnar deviation, pronated oblique) and repeat radiography in 10 to 14 days can improve sensitivity for scaphoid fractures. If a suspected scaphoid fracture cannot be confirmed with plain radiography, a bone scan or magnetic resonance imaging can be used. Subacute or chronic wrist pain usually develops gradually with or without a prior traumatic event. In these cases, the differential diagnosis is wide and includes tendinopathy and nerve entrapment. Overuse of the muscles of the forearm and wrist may lead to tendinopathy. Radial pain involving mostly the first extensor compartment is commonly de Quervain tenosynovitis. The diagnosis is based on history and examination findings of a positive Finkelstein test and a negative grind test. Nerve entrapment at the wrist presents with pain and also with sensory and sometimes motor symptoms. In ulnar neuropathies of the wrist, the typical presentation is wrist discomfort with sensory changes in the fourth and fifth digits. Activities that involve repetitive or prolonged wrist extension, such as cycling, karate, and baseball (specifically catchers), may increase the risk of ulnar neuropathy. Electrodiagnostic tests identify the area of nerve entrapment and the extent of the pathology.

  19. Development of a fluorescence-based sensor for rapid diagnosis of cyanide exposure.

    PubMed

    Jackson, Randy; Oda, Robert P; Bhandari, Raj K; Mahon, Sari B; Brenner, Matthew; Rockwood, Gary A; Logue, Brian A

    2014-02-01

    Although commonly known as a highly toxic chemical, cyanide is also an essential reagent for many industrial processes in areas such as mining, electroplating, and synthetic fiber production. The "heavy" use of cyanide in these industries, along with its necessary transportation, increases the possibility of human exposure. Because the onset of cyanide toxicity is fast, a rapid, sensitive, and accurate method for the diagnosis of cyanide exposure is necessary. Therefore, a field sensor for the diagnosis of cyanide exposure was developed based on the reaction of naphthalene dialdehyde, taurine, and cyanide, yielding a fluorescent β-isoindole. An integrated cyanide capture "apparatus", consisting of sample and cyanide capture chambers, allowed rapid separation of cyanide from blood samples. Rabbit whole blood was added to the sample chamber, acidified, and the HCN gas evolved was actively transferred through a stainless steel channel to the capture chamber containing a basic solution of naphthalene dialdehyde (NDA) and taurine. The overall analysis time (including the addition of the sample) was <3 min, the linear range was 3.13-200 μM, and the limit of detection was 0.78 μM. None of the potential interferents investigated (NaHS, NH4OH, NaSCN, and human serum albumin) produced a signal that could be interpreted as a false positive or a false negative for cyanide exposure. Most importantly, the sensor was 100% accurate in diagnosing cyanide poisoning for acutely exposed rabbits.

  20. Evaluation and diagnosis of wrist pain: a case-based approach.

    PubMed

    Shehab, Ramsey; Mirabelli, Mark H

    2013-04-15

    Patients with wrist pain commonly present with an acute injury or spontaneous onset of pain without a definite traumatic event. A fall onto an outstretched hand can lead to a scaphoid fracture, which is the most commonly fractured carpal bone. Conventional radiography alone can miss up to 30 percent of scaphoid fractures. Specialized views (e.g., posteroanterior in ulnar deviation, pronated oblique) and repeat radiography in 10 to 14 days can improve sensitivity for scaphoid fractures. If a suspected scaphoid fracture cannot be confirmed with plain radiography, a bone scan or magnetic resonance imaging can be used. Subacute or chronic wrist pain usually develops gradually with or without a prior traumatic event. In these cases, the differential diagnosis is wide and includes tendinopathy and nerve entrapment. Overuse of the muscles of the forearm and wrist may lead to tendinopathy. Radial pain involving mostly the first extensor compartment is commonly de Quervain tenosynovitis. The diagnosis is based on history and examination findings of a positive Finkelstein test and a negative grind test. Nerve entrapment at the wrist presents with pain and also with sensory and sometimes motor symptoms. In ulnar neuropathies of the wrist, the typical presentation is wrist discomfort with sensory changes in the fourth and fifth digits. Activities that involve repetitive or prolonged wrist extension, such as cycling, karate, and baseball (specifically catchers), may increase the risk of ulnar neuropathy. Electrodiagnostic tests identify the area of nerve entrapment and the extent of the pathology. PMID:23668446

  1. Robotic telepathology for intraoperative remote diagnosis using a still-imaging-based system.

    PubMed

    Demichelis, F; Barbareschi, M; Boi, S; Clemente, C; Dalla Palma, P; Eccher, C; Forti, S

    2001-11-01

    The aim of the present study was to assess whether a telemicroscopy system based on static imaging could provide a remote intraoperative frozen section service. Three pathologists evaluated 70 consecutive frozen section cases (for a total of 210 diagnoses) using a static telemicroscopy system (STeMiSy) and light microscopy (LM). STeMiSy uses a robotic microscope, enabling full remote control by consultant pathologists in a near real-time manner. Clinically important concordance between STeMiSy and LM was 98.6% (95.2% overall concordance), indicating very good agreement. The rates of deferred diagnoses given by STeMiSy and LM were comparable (11.0% and 9.5%, respectively). Compared with the consensus diagnosis, the diagnostic accuracy of STeMiSy and LM was 95.2% and 96.2%. The mean viewing time per slide was 3.6 minutes, and the overall time to make a diagnosis by STeMiSy was 6.2 minutes, conforming to intraoperative practice requirements. Our study demonstrates that a static imaging active telepathology system is comparable to dynamic telepathology systems and can provide a routine frozen section service.

  2. Recent advances in diagnosis and treatment of gliomas using chlorotoxin-based bioconjugates

    PubMed Central

    Cheng, Yongjun; Zhao, Jinhua; Qiao, Wenli; Chen, Kai

    2014-01-01

    Malignant gliomas, especially glioblastoma multiforme, are the most widely distributed and deadliest brain tumors because of their resistance to surgical and medical treatment. Research of glioma-specific bioconjugates for diagnosis and therapy developed rapidly during the past several years. Many studies have demonstrated that chlorotoxin (CTX) and Buthus martensii Karsch chlorotoxin (BmK CT) specifically inhibited glioma cells growth and metastasis, and accelerated tumor apoptosis. The bioconjugates of CTX or BmK CT with other molecules have played an increasing role in diagnostic imaging and treatment of gliomas. To date, CTX-based bioconjugates have achieved great success in phase I/II clinical trials about safety profiles. Here, we will provide a review on the important role of ion channels in the underlying mechanisms of gliomas invasive growth and how CTX suppresses gliomas proliferation and migration. We will summarize the recent advances in the applications of CTX bioconjugates for gliomas diagnosis and treatment. In addition, we will review recent studies on BmK CT bioconjugates and compare their efficacies with CTX derivatives. Finally, we will address advantages and challenges in the use of CTX or BmK CT bioconjugates as specific agents for theranostic applications in gliomas. PMID:25143859

  3. Effect of health belief model and health promotion model on breast cancer early diagnosis behavior: a systematic review.

    PubMed

    Ersin, Fatma; Bahar, Zuhal

    2011-01-01

    Breast cancer is an important public health problem on the grounds that it is frequently seen and it is a fatal disease. The objective of this systematic analysis is to indicate the effects of interventions performed by nurses by using the Health Belief Model (HBM) and Health Promotion Model (HPM) on the breast cancer early diagnosis behaviors and on the components of the Health Belief Model and Health Promotion Model. The reveiw was created in line with the Centre for Reviews and Dissemination guide dated 2009 (CRD) and developed by York University National Institute of Health Researches. Review was conducted by using PUBMED, OVID, EBSCO and COCHRANE databases. Six hundred seventy eight studies (PUBMED: 236, OVID: 162, EBSCO: 175, COCHRANE:105) were found in total at the end of the review. Abstracts and full texts of these six hundred seventy eight studies were evaluated in terms of inclusion and exclusion criteria and 9 studies were determined to meet the criteria. Samplings of the studies varied between ninety four and one thousand six hundred fifty five. It was detected in the studies that educations provided by taking the theories as basis became effective on the breast cancer early diagnosis behaviors. When the literature is examined, it is observed that the experimental researches which compare the concepts of Health Belief Model (HBM) and Health Promotion Model (HPM) preoperatively and postoperatively and show the effect of these concepts on education and are conducted by nurses are limited in number. Randomized controlled studies which compare HBM and HPM concepts preoperatively and postoperatively and show the efficiency of the interventions can be useful in evaluating the efficiency of the interventions.

  4. Advances in Diagnosis and Treatment of Fetal Alcohol Spectrum Disorders: From Animal Models to Human Studies.

    PubMed

    Murawski, Nathen J; Moore, Eileen M; Thomas, Jennifer D; Riley, Edward P

    2015-01-01

    Prenatal alcohol exposure can cause a number of physical, behavioral, cognitive, and neural impairments, collectively known as fetal alcohol spectrum disorders (FASD). This article examines basic research that has been or could be translated into practical applications for the diagnosis or treatment of FASD. Diagnosing FASD continues to be a challenge, but advances are being made at both basic science and clinical levels. These include identification of biomarkers, recognition of subtle facial characteristics of exposure, and examination of the relation between face, brain, and behavior. Basic research also is pointing toward potential new interventions for FASD involving pharmacotherapies, nutritional therapies, and exercise interventions. Although researchers have assessed the majority of these treatments in animal models of FASD, a limited number of recent clinical studies exist. An assessment of this literature suggests that targeted interventions can improve some impairments resulting from developmental alcohol exposure. However, combining interventions may prove more efficacious. Ultimately, advances in basic and clinical sciences may translate to clinical care, improving both diagnosis and treatment.

  5. Genetics of primary bilateral macronodular adrenal hyperplasia: a model for early diagnosis of Cushing's syndrome?

    PubMed

    Drougat, Ludivine; Espiard, Stéphanie; Bertherat, Jerôme

    2015-10-01

    Long-term consequences of cortisol excess are frequent despite appropriate treatment after cure of Cushing's syndrome. This might be due to diagnostic delay, often difficult to reduce in rare diseases. The identification of a genetic predisposing factor might help to improve early diagnosis by familial screening. Primary bilateral macronodular adrenal hyperplasia (PBMAH) is a rare cause of Cushing's syndrome. Hypercortisolism in PBMAH is most often diagnosed between the fifth and sixth decades of life. The bilateral nature of the adrenocortical tumors and the occurrence of rare clear familial forms suggest a genetic origin. Indeed, a limited subset of PBMAH can be observed as part of multiple tumors syndromes due to alterations of the APC, Menin or Fumarate Hydratase genes. Rare variants of the phosphodiesterases PDE11A have been associated with PBMAH. The recent identification of ARMC5 germline alterations in 25-50% of PBMAH patients without obvious familial history or associated tumors opens new perspectives. ARMC5 alterations follow the model of a tumor suppressor gene: a first germline inactivating mutation of this 16p located gene is followed by a somatic secondary hit on the other allele (inactivating mutation or allelic loss). Functional studies demonstrate that ARMC5 controls apoptosis and steroid synthesis. The phenotype of index cases patients with the mutation seems more severe than the one of WT index cases. However, phenotype variability within a family is often observed. This review summarizes the genetics of PBMAH, focusing on ARMC5, which offer new perspectives for early diagnosis of Cushing's syndrome.

  6. Vibration sensor-based bearing fault diagnosis using ellipsoid-ARTMAP and differential evolution algorithms.

    PubMed

    Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

    2014-06-16

    Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.

  7. Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

    PubMed Central

    Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

    2014-01-01

    Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately. PMID:24936949

  8. Serological assays based on recombinant viral proteins for the diagnosis of arenavirus hemorrhagic fevers.

    PubMed

    Fukushi, Shuetsu; Tani, Hideki; Yoshikawa, Tomoki; Saijo, Masayuki; Morikawa, Shigeru

    2012-10-12

    The family Arenaviridae, genus Arenavirus, consists of two phylogenetically independent groups: Old World (OW) and New World (NW) complexes. The Lassa and Lujo viruses in the OW complex and the Guanarito, Junin, Machupo, Sabia, and Chapare viruses in the NW complex cause viral hemorrhagic fever (VHF) in humans, leading to serious public health concerns. These viruses are also considered potential bioterrorism agents. Therefore, it is of great importance to detect these pathogens rapidly and specifically in order to minimize the risk and scale of arenavirus outbreaks. However, these arenaviruses are classified as BSL-4 pathogens, thus making it difficult to develop diagnostic techniques for these virus infections in institutes without BSL-4 facilities. To overcome these difficulties, antibody detection systems in the form of an enzyme-linked immunosorbent assay (ELISA) and an indirect immunofluorescence assay were developed using recombinant nucleoproteins (rNPs) derived from these viruses. Furthermore, several antigen-detection assays were developed. For example, novel monoclonal antibodies (mAbs) to the rNPs of Lassa and Junin viruses were generated. Sandwich antigen-capture (Ag-capture) ELISAs using these mAbs as capture antibodies were developed and confirmed to be sensitive and specific for detecting the respective arenavirus NPs. These rNP-based assays were proposed to be useful not only for an etiological diagnosis of VHFs, but also for seroepidemiological studies on VHFs. We recently developed arenavirus neutralization assays using vesicular stomatitis virus (VSV)-based pseudotypes bearing arenavirus recombinant glycoproteins. The goal of this article is to review the recent advances in developing laboratory diagnostic assays based on recombinant viral proteins for the diagnosis of VHFs and epidemiological studies on the VHFs caused by arenaviruses.

  9. 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

  10. The value of stereolithographic models for preoperative diagnosis of craniofacial deformities and planning of surgical corrections.

    PubMed

    Sailer, H F; Haers, P E; Zollikofer, C P; Warnke, T; Carls, F R; Stucki, P

    1998-10-01

    The purpose of this study was to assess the importance of stereolithographic models (SLMs) for preoperative diagnosis and planning in craniofacial surgery and to examine whether these models offer valuable additional information as compared to normal CT scans and 3D CT images. Craniofacial SLMs of 20 patients with craniomaxillofacial pathology were made. A helical volume CT scan of the anatomic area involved delivered the necessary data for their construction. These were built with an SLA 250 stereolithography apparatus (3D-Systems, Valencia, CA, USA), steered by FORM-IT/DCS software (University of Zurich, Switzerland). The stereolithography models were classified according to pathology, type of surgery and their relevance for surgical planning. Though not objectively measurable, it was beyond doubt that relevant additional information for the surgeon was obtained in cases of hypertelorism, severe asymmetries of the neuro- and viscerocranium, complex cranial synostoses and large skull defects. The value of these models as realistic "duplicates" of complex or rare dysmorphic craniofacial pathology for the purpose of creating a didactic collection should also be emphasized. The models proved to be less useful in cases of consolidated fractures of the periorbital and naso-ethmoidal complex, except where there was major dislocation.

  11. Should the diagnosis of COPD be based on a single spirometry test?

    PubMed Central

    Schermer, Tjard R; Robberts, Bas; Crockett, Alan J; Thoonen, Bart P; Lucas, Annelies; Grootens, Joke; Smeele, Ivo J; Thamrin, Cindy; Reddel, Helen K

    2016-01-01

    Clinical guidelines indicate that a chronic obstructive pulmonary disease (COPD) diagnosis is made from a single spirometry test. However, long-term stability of diagnosis based on forced expiratory volume in 1 s over forced vital capacity (FEV1/FVC) ratio has not been reported. In primary care subjects at risk for COPD, we investigated shifts in diagnostic category (obstructed/non-obstructed). The data were from symptomatic 40+ years (ex-)smokers referred for diagnostic spirometry, with three spirometry tests, each 12±2 months apart. The obstruction was based on post-bronchodilator FEV1/FVC < lower limit of normal (LLN) and <0.70 (fixed ratio). A total of 2,352 subjects (54% male, post-bronchodilator FEV1 76.5% predicted) were studied. By LLN definition, 32.2% were obstructed at baseline, but 32.2% of them were no longer obstructed at years 1 and/or 2. By fixed ratio, these figures were 46.6 and 23.8%, respectively. Overall, 14.3% of subjects changed diagnostic category by 1 year and 15.4% by 2 years when applying the LLN cut-off, and 15.1 and 14.6% by fixed ratio. Change from obstructed to non-obstructed was more likely for patients with higher body mass index (BMI) and baseline short-acting bronchodilator (SABA) users, and less likely for older subjects, those with lower FEV1% predicted, baseline inhaled steroid users, and current smokers or SABA users at year 1. Change from non-obstructed to obstructed was more likely for males, older subjects, current smokers and patients with lower baseline FEV1% predicted, and less likely for those with higher baseline BMI. Up to one-third of symptomatic (ex-)smokers with baseline obstruction on diagnostic spirometry had shifted to non-obstructed when routinely re-tested after 1 or 2 years. Given the implications for patients and health systems of a diagnosis of COPD, it should not be based on a single spirometry test. PMID:27684728

  12. Effective diagnosis of Alzheimer’s disease by means of large margin-based methodology

    PubMed Central

    2012-01-01

    Background Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer’s Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. Methods It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. Results Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. Conclusions All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes

  13. Sketch-based geologic modeling

    NASA Astrophysics Data System (ADS)

    Rood, M. P.; Jackson, M.; Hampson, G.; Brazil, E. V.; de Carvalho, F.; Coda, C.; Sousa, M. C.; Zhang, Z.; Geiger, S.

    2015-12-01

    Two-dimensional (2D) maps and cross-sections, and 3D conceptual models, are fundamental tools for understanding, communicating and modeling geology. Yet geologists lack dedicated and intuitive tools that allow rapid creation of such figures and models. Standard drawing packages produce only 2D figures that are not suitable for quantitative analysis. Geologic modeling packages can produce 3D models and are widely used in the groundwater and petroleum communities, but are often slow and non-intuitive to use, requiring the creation of a grid early in the modeling workflow and the use of geostatistical methods to populate the grid blocks with geologic information. We present an alternative approach to rapidly create figures and models using sketch-based interface and modelling (SBIM). We leverage methods widely adopted in other industries to prototype complex geometries and designs. The SBIM tool contains built-in geologic rules that constrain how sketched lines and surfaces interact. These rules are based on the logic of superposition and cross-cutting relationships that follow from rock-forming processes, including deposition, deformation, intrusion and modification by diagenesis or metamorphism. The approach allows rapid creation of multiple, geologically realistic, figures and models in 2D and 3D using a simple, intuitive interface. The user can sketch in plan- or cross-section view. Geologic rules are used to extrapolate sketched lines in real time to create 3D surfaces. Quantitative analysis can be carried our directly on the models. Alternatively, they can be output as simple figures or imported directly into other modeling tools. The software runs on a tablet PC and can be used in a variety of settings including the office, classroom and field. The speed and ease of use of SBIM enables multiple interpretations to be developed from limited data, uncertainty to be readily appraised, and figures and models to be rapidly updated to incorporate new data or concepts.

  14. Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

    NASA Astrophysics Data System (ADS)

    Cabrera, Diego; Sancho, Fernando; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Li, Chuan; Vásquez, Rafael E.

    2015-09-01

    This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal's condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients' energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters' space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.

  15. Formal Methods for Automated Diagnosis of Autosub 6000

    NASA Technical Reports Server (NTRS)

    Ernits, Juhan; Dearden, Richard; Pebody, Miles

    2009-01-01

    This is a progress report on applying formal methods in the context of building an automated diagnosis and recovery system for Autosub 6000, an Autonomous Underwater Vehicle (AUV). The diagnosis task involves building abstract models of the control system of the AUV. The diagnosis engine is based on Livingstone 2, a model-based diagnoser originally built for aerospace applications. Large parts of the diagnosis model can be built without concrete knowledge about each mission, but actual mission scripts and configuration parameters that carry important information for diagnosis are changed for every mission. Thus we use formal methods for generating the mission control part of the diagnosis model automatically from the mission script and perform a number of invariant checks to validate the configuration. After the diagnosis model is augmented with the generated mission control component model, it needs to be validated using verification techniques.

  16. A method of real-time fault diagnosis for power transformers based on vibration analysis

    NASA Astrophysics Data System (ADS)

    Hong, Kaixing; Huang, Hai; Zhou, Jianping; Shen, Yimin; Li, Yujie

    2015-11-01

    In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.

  17. Gearbox fault diagnosis based on time-frequency domain synchronous averaging and feature extraction technique

    NASA Astrophysics Data System (ADS)

    Zhang, Shengli; Tang, Jiong

    2016-04-01

    Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.

  18. A smartphone-based automatic diagnosis system for facial nerve palsy.

    PubMed

    Kim, Hyun Seok; Kim, So Young; Kim, Young Ho; Park, Kwang Suk

    2015-10-21

    Facial nerve palsy induces a weakness or loss of facial expression through damage of the facial nerve. A quantitative and reliable assessment system for facial nerve palsy is required for both patients and clinicians. In this study, we propose a rapid and portable smartphone-based automatic diagnosis system that discriminates facial nerve palsy from normal subjects. Facial landmarks are localized and tracked by an incremental parallel cascade of the linear regression method. An asymmetry index is computed using the displacement ratio between the left and right side of the forehead and mouth regions during three motions: resting, raising eye-brow and smiling. To classify facial nerve palsy, we used Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), and Leave-one-out Cross Validation (LOOCV) with 36 subjects. The classification accuracy rate was 88.9%.

  19. Supporting diagnosis and treatment in medical care based on Big Data processing.

    PubMed

    Lupşe, Oana-Sorina; Crişan-Vida, Mihaela; Stoicu-Tivadar, Lăcrămioara; Bernard, Elena

    2014-01-01

    With information and data in all domains growing every day, it is difficult to manage and extract useful knowledge for specific situations. This paper presents an integrated system architecture to support the activity in the Ob-Gin departments with further developments in using new technology to manage Big Data processing - using Google BigQuery - in the medical domain. The data collected and processed with Google BigQuery results from different sources: two Obstetrics & Gynaecology Departments, the TreatSuggest application - an application for suggesting treatments, and a home foetal surveillance system. Data is uploaded in Google BigQuery from Bega Hospital Timişoara, Romania. The analysed data is useful for the medical staff, researchers and statisticians from public health domain. The current work describes the technological architecture and its processing possibilities that in the future will be proved based on quality criteria to lead to a better decision process in diagnosis and public health.

  20. Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis

    PubMed Central

    Li, Rongjian; Zhang, Wenlu; Suk, Heung-Il; Wang, Li; Li, Jiang; Shen, Dinggang; Ji, Shuiwang

    2015-01-01

    Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multi-modality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of convolutional neural networks, where the input and output are two volumetric modalities. The network contains a large number of trainable parameters that capture the relationship between input and output modalities. When trained on subjects with all modalities, the network can estimate the output modality given the input modality. We evaluated our method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, where the input and output modalities are MRI and PET images, respectively. Results showed that our method significantly outperformed prior methods. PMID:25320813

  1. Psychiatric polypharmacy: a clinical approach based on etiology and differential diagnosis.

    PubMed

    Freudenreich, Oliver; Kontos, Nicholas; Querques, John

    2012-01-01

    Polypharmacy is common clinical practice in the United States for many psychiatric conditions and for many reasons. In this article we encourage clinicians to use the familiar practice of differential diagnosis to systematically identify etiological factors contributing to polypharmacy. We offer a clinical approach based on (1) reviewing the four main factors responsible for polypharmacy (the disease, the patient, the physician, and society) and (2) answering two questions about optimizing medication regimens (What can I do without explicit permission from the patient or others? What can I do with permission from them?). We contend that all physicians share a professional responsibility for prescribing medications judiciously because unnecessary prescribing exposes patients to unwarranted risks and squanders valuable and scarce resources. Psychiatrists can ask themselves a Kantian question: would my way of prescribing lead to good, socially acceptable outcomes if followed by all physicians treating similar patients? PMID:22512741

  2. Graphene based aptasensor for glycated albumin in diabetes mellitus diagnosis and monitoring.

    PubMed

    Apiwat, Chayachon; Luksirikul, Patraporn; Kankla, Pacharapon; Pongprayoon, Prapasiri; Treerattrakoon, Kiatnida; Paiboonsukwong, Kittiphong; Fucharoen, Suthat; Dharakul, Tararaj; Japrung, Deanpen

    2016-08-15

    We selected and modified DNA aptamers specifically bound glycated human serum albumin (GHSA), which is an intermediate marker for diabetes mellitus. Our aptamer truncation study indicated that the hairpin-loop structure with 23 nucleotides length containing triple G-C hairpins and 15-nucleotide loop, plays an important role in GHSA binding. Fluorescent quenching graphene oxide (GO) and Cy5-labeled G8 aptamer were used in this study to develop simple and sensitive graphene based aptasensor for GHSA detection. The limit of detection (LOD) of our aptasensor was 50 μg/mL, which was lower than other existing methods. In addition, with the nuclease resistance system, our GHSA detection platform could also be used in clinical samples. Importantly, our approach could significantly reveal the higher levels of GHSA concentrations in diabetes than normal serums. These indicate that our aptasensor has a potential for diagnosis and monitoring of diabetes mellitus. PMID:27084987

  3. Online damage diagnosis for civil infrastructure employing a flexibility-based approach

    NASA Astrophysics Data System (ADS)

    Gao, Y.; Spencer, B. F., Jr.

    2006-02-01

    Structural health monitoring (SHM) and damage detection have recently emerged as a new research area in civil engineering. Continuous and long-term monitoring of civil infrastructure is desirable, because it allows the damage in the structure to be detected at an early stage so that necessary measures can be carried out to prolong and optimize the associated service life and cost. In this paper, an approach which extends a flexibility-based damage detection technique, the damage locating vector (DLV) method, for continuous online SHM is presented. The essence of the proposed approach is to construct an approximate flexibility matrix for the damaged structure utilizing the modal normalization constants from the undamaged structure. This extended DLV method can then be applied for online damage diagnosis. Numerical simulation has been conducted using a 14-bay planar truss structure, with the results showing that the proposed approach works well for both single- and multiple-damage scenarios.

  4. Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum

    PubMed Central

    Lv, Yong; Zhu, Qinglin; Yuan, Rui

    2015-01-01

    The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. PMID:25585105

  5. Supporting diagnosis and treatment in medical care based on Big Data processing.

    PubMed

    Lupşe, Oana-Sorina; Crişan-Vida, Mihaela; Stoicu-Tivadar, Lăcrămioara; Bernard, Elena

    2014-01-01

    With information and data in all domains growing every day, it is difficult to manage and extract useful knowledge for specific situations. This paper presents an integrated system architecture to support the activity in the Ob-Gin departments with further developments in using new technology to manage Big Data processing - using Google BigQuery - in the medical domain. The data collected and processed with Google BigQuery results from different sources: two Obstetrics & Gynaecology Departments, the TreatSuggest application - an application for suggesting treatments, and a home foetal surveillance system. Data is uploaded in Google BigQuery from Bega Hospital Timişoara, Romania. The analysed data is useful for the medical staff, researchers and statisticians from public health domain. The current work describes the technological architecture and its processing possibilities that in the future will be proved based on quality criteria to lead to a better decision process in diagnosis and public health. PMID:24743079

  6. Fetal Cell Based Prenatal Diagnosis: Perspectives on the Present and Future

    PubMed Central

    Fiddler, Morris

    2014-01-01

    The ability to capture and analyze fetal cells from maternal circulation or other sources during pregnancy has been a goal of prenatal diagnostics for over thirty years. The vision of replacing invasive prenatal diagnostic procedures with the prospect of having the entire fetal genome in hand non-invasively for chromosomal and molecular studies for both clinical and research use has brought many investigators and innovations into the effort. While the object of this desire, however, has remained elusive, the aspiration for this approach to non-invasive prenatal diagnosis remains and the inquiry has continued. With the advent of screening by cell-free DNA analysis, the standards for fetal cell based prenatal diagnostics have been sharpened. Relevant aspects of the history and the current status of investigations to meet the goal of having an accessible and reliable strategy for capturing and analyzing fetal cells during pregnancy are reviewed. PMID:26237488

  7. Psychovegetative syndrome diagnosis: an automated psychophysiological investigation and mathematical modeling approach.

    PubMed

    Brelidze, Z; Samadashvili, Z; Khachapuridze, G; Kubaneishvili, E; Nozadze, Z; Benidze, I; Tsitskishvili, N

    1995-01-01

    1. INTRODUCTION. The main purpose of our work was to create the informational expert system of psychovegetative syndrome diagnosis by applying clinical data and estimating the functioning of the central and peripheral part of regulatory apparatus of the human organism, taking into consideration parallel and consecutive sensory, motor, associative, emotional drive systems, and internal body state. We used automatized psychophysiological investigation and mathematical models. For this purpose the following principal tasks have been prepared: the creation of database of quantifiable estimation patient state; the definition and automation of psychophysiological investigation; mathematical modeling of vegetative functions using a non-invasive sample and its connection with real psychophysiological experiment; mathematical modeling of organisms inner medium homeostasis; and the creation of an informational-expert system of psychovegetative syndrome diagnosis. 2. DATABASE OF ESTIMATION OF PATIENTS STATE. The medical records of the DB "PATIENT" contain data on patient psychic and somatoneurological status. 3. AUTOMATED PSYCHOPHYSIOLOGICAL INVESTIGATION. Psychophysiological investigation enables estimation of the functioning of several subsystems of the human organism and establishes an interrelationship between them by means of electrophysiological data and performance parameters. The study of psychophysiological provision of behavior by psychophysiological investigation enables us to get information about adaptational mechanisms of the patient under certain environmental loads. By means of special mathematical provision, the mathematical elaboration of biosignals as performance parameters has been realized; also realized were the formation of received parameters in the database, the estimation of separated parameters in the view of informativity, and the establishment of diagnostic patterns. 4. MATHEMATICAL MODELS FOR ESTIMATION INTERNAL BODY STATE. The proposed

  8. Privacy-Preserving Self-Helped Medical Diagnosis Scheme Based on Secure Two-Party Computation in Wireless Sensor Networks

    PubMed Central

    Wen, Qiaoyan; Zhang, Yudong; Li, Wenmin

    2014-01-01

    With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients' health information and doctors' diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions. PMID:25126107

  9. [Phenylketonuria as a model system for DNA diagnosis of hereditary disorders].

    PubMed

    Meijer, H; Hekking, M; van den Enden, A T; Jongbloed, R J; Schrander-Stumpel, C T; Geraedts, J P

    1990-10-01

    Phenylketonuria (PKU), due to a defect in phenylalanine hydroxylase (PAH), is presented as a model system for computer-aided DNA diagnosis of genetic diseases. Eight different restriction fragment length polymorphism (RFLP) markers have been localized within the introns of the 90 kb PAH gene (located on chromosome 12). These RFLPs can be combined in 384 different ways and each combination has been defined as a particular haplotype. A special computer program has been developed to calculate the possible haplotype combinations in a PKU core family (index patient and parents), with the goal to derive unambiguously both the PAH and PKU alleles. Taking into account that participation of other members of the family (grandparents or brothers/sisters) is sometimes necessary, haplotyping by itself is sufficient to establish (or exclude) the PKU status of an individual in approximately eight out of ten PKU families.

  10. Diagnosis and treatment of acute ankle injuries: development of an evidence-based algorithm

    PubMed Central

    Polzer, Hans; Kanz, Karl Georg; Prall, Wolf Christian; Haasters, Florian; Ockert, Ben; Mutschler, Wolf; Grote, Stefan

    2011-01-01

    Acute ankle injuries are among the most common injuries in emergency departments. However, there are still no standardized examination procedures or evidence-based treatment. Therefore, the aim of this study was to systematically search the current literature, classify the evidence, and develop an algorithm for the diagnosis and treatment of acute ankle injuries. We systematically searched PubMed and the Cochrane Database for randomized controlled trials, meta-analyses, systematic reviews or, if applicable, observational studies and classified them according to their level of evidence. According to the currently available literature, the following recommendations have been formulated: i) the Ottawa Ankle/Foot Rule should be applied in order to rule out fractures; ii) physical examination is sufficient for diagnosing injuries to the lateral ligament complex; iii) classification into stable and unstable injuries is applicable and of clinical importance; iv) the squeeze-, crossed leg- and external rotation test are indicative for injuries of the syndesmosis; v) magnetic resonance imaging is recommended to verify injuries of the syndesmosis; vi) stable ankle sprains have a good prognosis while for unstable ankle sprains, conservative treatment is at least as effective as operative treatment without the related possible complications; vii) early functional treatment leads to the fastest recovery and the least rate of reinjury; viii) supervised rehabilitation reduces residual symptoms and re-injuries. Taken these recommendations into account, we present an applicable and evidence-based, step by step, decision pathway for the diagnosis and treatment of acute ankle injuries, which can be implemented in any emergency department or doctor's practice. It provides quality assurance for the patient and promotes confidence in the attending physician. PMID:22577506

  11. Rapid diagnosis of Argentine hemorrhagic fever by reverse transcriptase PCR-based assay.

    PubMed Central

    Lozano, M E; Enría, D; Maiztegui, J I; Grau, O; Romanowski, V

    1995-01-01

    Argentine hemorrhagic fever (AHF) is an endemo-epidemic disease caused by Junín virus. This report demonstrates that a reverse transcriptase (RT) PCR-based assay developed in our laboratory to detect Junín virus in whole blood samples is sensitive and specific. The experiments were conducted in a double-blinded manner using 94 clinical samples collected in the area in which AHF is endemic. The RT-PCR-based assay was compared with traditional methodologies, including enzyme-linked immunosorbent assay, plaque neutralization tests, and occasionally viral isolation. The calculated parameters for RT-PCR diagnosis, with seroconversion as the "gold standard," were 98% sensitivity and 76% specificity. It is noteworthy that 94% of the patients with putative false-positive results (RT-PCR positive and no seroconversion detected) exhibited febrile syndromes of undefined etiology. These results could be interpreted to mean that most of those patients with febrile syndromes were actually infected with Junín virus but did not develop a detectable immune response. Furthermore, 8 laboratory-fabricated samples and 25 blood samples of patients outside the area in which AHF is endemic tested in a similar way were disclosed correctly (100% match). The RT-PCR assay is the only laboratory test available currently for the early and rapid diagnosis of AHF. It is sensitive enough to detect the low viremia found during the period in which immune plasma therapy can be used effectively, reducing mortality rates from 30% to less than 1%. PMID:7542268

  12. Real time diagnosis of bladder cancer with probe-based confocal laser endomicroscopy

    NASA Astrophysics Data System (ADS)

    Liu, Jen-Jane; Wu, Katherine; Adams, Winifred; Hsiao, Shelly T.; Mach, Kathleen E.; Beck, Andrew H.; Jensen, Kristin C.; Liao, Joseph C.

    2011-02-01

    Probe-based confocal laser endomicroscopy (pCLE) is an emerging technology for in vivo optical imaging of the urinary tract. Particularly for bladder cancer, real time optical biopsy of suspected lesions will likely lead to improved management of bladder cancer. With pCLE, micron scale resolution is achieved with sterilizable imaging probes (1.4 or 2.6 mm diameter), which are compatible with standard cystoscopes and resectoscopes. Based on our initial experience to date (n = 66 patients), we have demonstrated the safety profile of intravesical fluorescein administration and established objective diagnostic criteria to differentiate between normal, benign, and neoplastic urothelium. Confocal images of normal bladder showed organized layers of umbrella cells, intermediate cells, and lamina propria. Low grade bladder cancer is characterized by densely packed monomorphic cells with central fibrovascular cores, whereas high grade cancer consists of highly disorganized microarchitecture and pleomorphic cells with indistinct cell borders. Currently, we are conducting a diagnostic accuracy study of pCLE for bladder cancer diagnosis. Patients scheduled to undergo transurethral resection of bladder tumor are recruited. Patients undergo first white light cystocopy (WLC), followed by pCLE, and finally histologic confirmation of the resected tissues. The diagnostic accuracy is determined both in real time by the operative surgeon and offline after additional image processing. Using histology as the standard, the sensitivity, specificity, positive and negative predictive value of WLC and WLC + pCLE are calculated. With additional validation, pCLE may prove to be a valuable adjunct to WLC for real time diagnosis of bladder cancer.

  13. NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer

    PubMed Central

    Lin, Yan; Ma, Changchun; Liu, Chengkang; Wang, Zhening; Yang, Jurong; Liu, Xinmu; Shen, Zhiwei; Wu, Renhua

    2016-01-01

    Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients. PMID:27107423

  14. Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images

    PubMed Central

    2012-01-01

    Background Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. Method The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. Results The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice. PMID:22236465

  15. A fault diagnosis approach for diesel engine valve train based on improved ITD and SDAG-RVM

    NASA Astrophysics Data System (ADS)

    Yu, Liu; Junhong, Zhang; Fengrong, Bi; Jiewei, Lin; Wenpeng, Ma

    2015-02-01

    Targeting the non-stationary characteristics of the vibration signals of a diesel engine valve train, and the limitation of the autoregressive (AR) model, a novel approach based on the improved intrinsic time-scale decomposition (ITD) and relevance vector machine (RVM) is proposed in this paper for the identification of diesel engine valve train faults. The approach mainly consists of three stages: First, prior to the feature extraction, non-uniform B-spline interpolation is introduced to the ITD method for the fitting of baseline signal, then the improved ITD is used to decompose the non-stationary signals into a set of stationary proper rotation components (PRCs). Second, the AR model is established for each PRC, and the first several AR coefficients together with the remnant variance of all PRCs are regarded as the fault feature vectors. Finally, a new separability based directed acyclic graph (SDAG) method is proposed to determine the structure of multi-class RVM, and the fault feature vectors are classified using the SDAG-RVM classifier to recognize the fault of the diesel engine valve train. The experimental results demonstrate that the proposed fault diagnosis approach can effectively extract the fault features and accurately identify the fault patterns.

  16. Essentials in the diagnosis of acid-base disorders and their high altitude application.

    PubMed

    Paulev, P E; Zubieta-Calleja, G R

    2005-09-01

    This report describes the historical development in the clinical application of chemical variables for the interpretation of acid-base disturbances. The pH concept was already introduced in 1909. Following World War II, disagreements concerning the definition of acids and bases occurred, and since then two strategies have been competing. Danish scientists in 1923 defined an acid as a substance able to give off a proton at a given pH, and a base as a substance that could bind a proton, whereas the North American Singer-Hasting school in 1948 defined acids as strong non-buffer anions and bases as non-buffer cations. As a consequence of this last definition, electrolyte disturbances were mixed up with real acid-base disorders and the variable, strong ion difference (SID), was introduced as a measure of non-respiratory acid-base disturbances. However, the SID concept is only an empirical approximation. In contrast, the Astrup/Siggaard-Andersen school of scientists, using computer strategies and the Acid-base Chart, has made diagnosis of acid-base disorders possible at a glance on the Chart, when the data are considered in context with the clinical development. Siggaard-Andersen introduced Base Excess (BE) or Standard Base Excess (SBE) in the extracellular fluid volume (ECF), extended to include the red cell volume (eECF), as a measure of metabolic acid-base disturbances and recently replaced it by the term Concentration of Titratable Hydrogen Ion (ctH). These two concepts (SBE and ctH) represent the same concentration difference, but with opposite signs. Three charts modified from the Siggaard-Andersen Acid-Base Chart are presented for use at low, medium and high altitudes of 2500 m, 3500 m, and 4000 m, respectively. In this context, the authors suggest the use of Titratable Hydrogen Ion concentration Difference (THID) in the extended extracellular fluid volume, finding it efficient and better than any other determination of the metabolic component in acid-base

  17. Essentials in the diagnosis of acid-base disorders and their high altitude application.

    PubMed

    Paulev, P E; Zubieta-Calleja, G R

    2005-09-01

    This report describes the historical development in the clinical application of chemical variables for the interpretation of acid-base disturbances. The pH concept was already introduced in 1909. Following World War II, disagreements concerning the definition of acids and bases occurred, and since then two strategies have been competing. Danish scientists in 1923 defined an acid as a substance able to give off a proton at a given pH, and a base as a substance that could bind a proton, whereas the North American Singer-Hasting school in 1948 defined acids as strong non-buffer anions and bases as non-buffer cations. As a consequence of this last definition, electrolyte disturbances were mixed up with real acid-base disorders and the variable, strong ion difference (SID), was introduced as a measure of non-respiratory acid-base disturbances. However, the SID concept is only an empirical approximation. In contrast, the Astrup/Siggaard-Andersen school of scientists, using computer strategies and the Acid-base Chart, has made diagnosis of acid-base disorders possible at a glance on the Chart, when the data are considered in context with the clinical development. Siggaard-Andersen introduced Base Excess (BE) or Standard Base Excess (SBE) in the extracellular fluid volume (ECF), extended to include the red cell volume (eECF), as a measure of metabolic acid-base disturbances and recently replaced it by the term Concentration of Titratable Hydrogen Ion (ctH). These two concepts (SBE and ctH) represent the same concentration difference, but with opposite signs. Three charts modified from the Siggaard-Andersen Acid-Base Chart are presented for use at low, medium and high altitudes of 2500 m, 3500 m, and 4000 m, respectively. In this context, the authors suggest the use of Titratable Hydrogen Ion concentration Difference (THID) in the extended extracellular fluid volume, finding it efficient and better than any other determination of the metabolic component in acid-base

  18. Hospital payment systems based on diagnosis-related groups: experiences in low- and middle-income countries

    PubMed Central

    Wittenbecher, Friedrich

    2013-01-01

    Abstract Objective This paper provides a comprehensive overview of hospital payment systems based on diagnosis-related groups (DRGs) in low- and middle-income countries. It also explores design and implementation issues and the related challenges countries face. Methods A literature research for papers on DRG-based payment systems in low- and middle-income countries was conducted in English, French and Spanish through Pubmed, the Pan American Health Organization’s Regional Library of Medicine and Google. Findings Twelve low- and middle-income countries have DRG-based payment systems and another 17 are in the piloting or exploratory stage. Countries have chosen from a wide range of imported and self-developed DRG models and most have adapted such models to their specific contexts. All countries have set expenditure ceilings. In general, systems were piloted before being implemented. The need to meet certain requirements in terms of coding standardization, data availability and information technology made implementation difficult. Private sector providers have not been fully integrated, but most countries have managed to delink hospital financing from public finance budgeting. Conclusion Although more evidence on the impact of DRG-based payment systems is needed, our findings suggest that (i) the greater portion of health-care financing should be public rather than private; (ii) it is advisable to pilot systems first and to establish expenditure ceilings; (iii) countries that import an existing variant of a DRG-based system should be mindful of the need for adaptation; and (iv) countries should promote the cooperation of providers for appropriate data generation and claims management. PMID:24115798

  19. Automated Decomposition of Model-based Learning Problems

    NASA Technical Reports Server (NTRS)

    Williams, Brian C.; Millar, Bill

    1996-01-01

    A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of [\\em decompositional model-based learning (DML)], a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.

  20. [Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status].

    PubMed

    Tan, Chang-Wei; Zhou, Qing-Bo; Qi, La; Zhuang, Heng-Yang

    2008-06-01

    The correlations of rice plant nitrogen content with raw hyperspectral reflectance, first derivative hyperspectral reflectance, and hyperspectral characteristic parameters were analyzed, and the hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status with these remote sensing parameters as independent variables were constructed and validated. The results indicated that the nitrogen content in rice plant organs had a variation trend of stem < sheath < spike < leaf. The spectral reflectance at visible light bands was leaf < spike < sheath < stem, but that at near-infrared bands was in adverse. The linear and exponential models with the raw hyperspectral reflectance at 796.7 nm and the first derivative hyperspectral reflectance at 738.4 nm as independent variables could better diagnose rice plant nitrogen nutritional status, with the decisive coefficients (R2) being 0.7996 and 0.8606, respectively; while the model with vegetation index (SDr - SDb) / (SDr + SDb) as independent variable, i. e., y = 365.871 + 639.323 ((SDr - SDb) / (SDr + SDb)), was most fit rice plant nitrogen content, with R2 = 0.8755, RMSE = 0.2372 and relative error = 11.36%, being able to quantitatively diagnose the nitrogen nutritional status of rice.

  1. Evidence-based diagnosis, health care, and rehabilitation for children with cerebral palsy.

    PubMed

    Novak, Iona

    2014-08-01

    Safer and more effective interventions have been invented for children with cerebral palsy, but the rapid expansion of the evidence base has made keeping up-to-date difficult. Unfortunately, outdated care is being provided. The aims were to survey the questions parents asked neurologists and provide evidence-based answers, using knowledge translation techniques. Parents asked the following questions: (1) what's wrong with my baby? An algorithm for early diagnosis was proposed. (2) What is cerebral palsy and what online resources are current? Reputable information websites were sourced and hyperlinks provided. (3) The prognosis? Prognostic data from meta-analyses were summarized in an infographic. (4) What interventions offer the most evidence-supported results? Systematic review data about the most effective interventions was mapped into a bubble chart infographic. Finally, (5) What can we expect? Predictors and facilitators of good outcomes were summarized. This article provides an overview of the most up-to-date diagnostic practices and evidence-based intervention options.

  2. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

    2016-03-01

    As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Numerous publications about the study and applications of WT for RMFD have been presented to academic journals, technical reports and conference proceedings. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation and field experiments. Then the development process of WT based on inner product is concluded and the applications of major developments in RMFD are also summarized. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed. It is expected that this paper can offer an in-depth and comprehensive references for researchers and help them with finding out further research topics.

  3. Diagnosis and mortality in prehospital emergency patients transported to hospital: a population-based and registry-based cohort study

    PubMed Central

    Christensen, Erika Frischknecht; Larsen, Thomas Mulvad; Jensen, Flemming Bøgh; Bendtsen, Mette Dahl; Hansen, Poul Anders; Johnsen, Søren Paaske; Christiansen, Christian Fynbo

    2016-01-01

    Objective Knowledge about patients after calling for an ambulance is limited to subgroups, such as patients with cardiac arrest, myocardial infarction, trauma and stroke, while population-based studies including all diagnoses are few. We examined the diagnostic pattern and mortality among all patients brought to hospital by ambulance after emergency calls. Design Registry-based cohort study. Setting and participants We included patients brought to hospital in an ambulance dispatched after emergency calls during 2007–2014 in the North Denmark Region (580 000 inhabitants). We reported hospital diagnosis according to the chapters of the International Classification of Diseases, 10th Edition (ICD-10), and studied death on days 1 and 30 after the call. Cohort characteristics and diagnoses were described, and the Kaplan-Meier method was used to estimate mortality and 95% CIs. Results In total, 148 757 patients were included, mean age 52.9 (SD 24.3) years. The most frequent ICD-10 diagnosis chapters were: ‘injury and poisoning’ (30.0%), and the 2 non-specific diagnosis chapters: ‘symptoms and abnormal findings, not elsewhere classified’ (17.5%) and ‘factors influencing health status and contact with health services’ (14.1%), followed by ‘diseases of the circulatory system’ (10.6%) and ‘diseases of the respiratory system’ (6.7%). The overall 1-day mortality was 1.8% (CI 1.7% to 1.8%) and 30-day mortality 4.7% (CI 4.6% to 4.8%). ‘Diseases of the circulatory system’ had the highest 1-day mortality of 7.7% (CI 7.3% to 8.1%) accounting for 1209 deaths. After 30 days, the highest number of deaths were among circulatory diseases (2313), respiratory diseases (1148), ‘symptoms and abnormal findings, not elsewhere classified’ (1119) and ‘injury and poisoning’ (741), and 30 days mortality in percentage was 14.7%, 11.6%, 4.3% and 1.7%, respectively. Conclusions Patients' diagnoses from hospital stay after calling 1-1-2 in this population-based

  4. Teaching Musculoskeletal Physical Diagnosis Using A Web-based Tutorial and Pathophysiology-Focused Cases

    PubMed Central

    Modica, Renee F; Thundiyil, Josef G; Chou, Calvin; Diab, Mohammad; Von Scheven, Emily

    2009-01-01

    Objective: To assess the effectiveness of an experimental curriculum on teaching first-year medical students the musculoskeletal exam as compared to a traditional curriculum. Background: Musculoskeletal complaints are common in the primary care setting. Practitioners are often deficient in examination skills and knowledge regarding musculoskeletal diseases. There is a lack of uniformity regarding how to teach the musculoskeletal examination among sub-specialists. We propose a novel web-based approach to teaching the musculoskeletal exam that is enhanced by peer practice with pathophysiology-focused cases. We sought to assess the effectiveness of an innovative musculoskeletal curriculum on the knowledge and skills of first-year medical students related to musculoskeletal physical diagnosis as compared to a traditional curriculum. The secondary purpose of this study was to assess satisfaction of students and preceptors exposed to this teaching method. Methods: This quasi-experimental study was conducted at a single LCME-accredited medical school and included a convenience sample from 2 consecutive classes of medical students during the musculoskeletal portion of their physical diagnosis class. We conducted a needs assessment of the traditional curriculum used to teach musculoskeletal examination. The needs assessment informed the development of an experimental curriculum. One class (control group) received the traditional curriculum while the second class (experimental group) received the experimental curriculum, consisting of a web-based musculoskeletal tutorial, pathophysiology-focused cases, and facilitator preparation. We used multiple-choice questions and musculoskeletal OSCE scores to assess differences between knowledge and skills in the 2 groups. Results: The sample consisted of 140 students in each medical school class. There were no statistically significant differences between the 2 groups. One hundred seven students from the control group and 120 students

  5. DIABETES, OBESITY AND DIAGNOSIS OF AMYOTROPHIC LATERAL SCLEROSIS: A POPULATION-BASED STUDY

    PubMed Central

    Kioumourtzoglou, Marianthi-Anna; Rotem, Ran S.; Seals, Ryan M.; Gredal, Ole; Hansen, Johnni; Weisskopf, Marc G.

    2016-01-01

    Importance Although prior studies have suggested a role of cardiometabolic health on pathogenesis of amyotrophic lateral sclerosis (ALS), the association with diabetes has not been widely examined. Objective Amyotrophic lateral sclerosis is the most common motor neuron disorder. Several vascular risk factors have been associated with decreased risk for ALS. Although diabetes is also a risk factor for vascular disease, the few studies of diabetes and ALS have been inconsistent. We examined the association between diabetes and obesity, each identified through ICD-8 or 10 codes in a hospital registry, and ALS using data from the Danish National Registers. Design and Setting Population-based nested case-control study. Participants 3,650 Danish residents diagnosed with ALS between 1982 and 2009, and 365,000 controls (100 for each ALS case), matched on age and sex. Main Outcome Measure Adjusted odds ratio (OR) for ALS associated with diabetes or obesity diagnoses at least three years prior to the ALS diagnosis date. Results When considering diabetes and our obesity indicator together, the estimated OR for ALS was 0.61 (95%CI: 0.46–0.80) for diabetes and 0.81 (95%CI: 0.57–1.16) for obesity. We observed no effect modification on the association with diabetes by gender, but a significant modification by age at first diabetes or age at ALS, with the protective association stronger with increasing age, consistent with different associations by diabetes type. Conclusions and Relevance We conducted a nationwide study to investigate the association between diabetes and ALS diagnosis. Our findings are in agreement with previous reports of a protective association between vascular risk factors and ALS, and suggest type 2 diabetes, but not type 1, is protective for ALS. PMID:26030836

  6. Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images

    PubMed Central

    Ozkan, Murat; Cakiroglu, Murat; Kocaman, Orhan; Kurt, Mevlut; Yilmaz, Bulent; Can, Guray; Korkmaz, Ugur; Dandil, Emre; Eksi, Ziya

    2016-01-01

    Aim: The aim was to develop a high-performance computer-aided diagnosis (CAD) system with image processing and pattern recognition in diagnosing pancreatic cancer by using endosonography images. Materials and Methods: On the images, regions of interest (ROI) of three groups of patients (<40, 40-60 and >60) were extracted by experts; features were obtained from images using three different techniques and were trained separately for each age group with an Artificial Neural Network (ANN) to diagnose cancer. The study was conducted on endosonography images of 202 patients with pancreatic cancer and 130 noncancer patients. Results: 122 features were identified from the 332 endosonography images obtained in the study, and the 20 most appropriate features were selected by using the relief method. Images classified under three age groups (in years; <40, 40-60 and >60) were tested via 200 random tests and the following ratios were obtained in the classification: accuracy: 92%, 88.5%, and 91.7%, respectively; sensitivity: 87.5%, 85.7%, and 93.3%, respectively; and specificity: 94.1%, 91.7%, and 88.9%, respectively. When all the age groups were assessed together, the following values were obtained: accuracy: 87.5%, sensitivity: 83.3%, and specificity: 93.3%. Conclusions: It was observed that the CAD system developed in the study performed better in diagnosing pancreatic cancer images based on classification by patient age compared to diagnosis without classification. Therefore, it is imperative to take patient age into consideration to ensure higher performance. PMID:27080608

  7. Computer-aided diagnosis system for lung cancer based on retrospective helical CT image

    NASA Astrophysics Data System (ADS)

    Ukai, Yuji; Niki, Noboru; Satoh, Hitoshi; Eguchi, Kenji; Mori, Kiyoshi; Ohmatsu, Hironobu; Kakinuma, Ryutaro; Kaneko, Masahiro; Moriyama, Noriyuki

    2000-06-01

    In this paper, we present a computer-aided diagnosis (CAD) system for lung cancer to detect nodule candidates at an early stage from the present and the early helical CT screening of the thorax. We developed an algorithm that can compare automatically the slice images of present and early CT scans for the assistance of comparative reading in retrospect. The algorithm consists of the ROI detection and shape analysis based on comparison of each slice image in the present and the early CT scans. The slice images of present and early CT scans are both displayed in parallel and analyzed quantitatively in order to detect the changes in size and intensity affection. We validated the efficiency of this algorithm by application to image data for mass screening of 50 subjects (total: 150 CT scans). The algorithm could compare the slice images correctly in most combinations with respect to physician's point of view. We validated the efficiency of the algorithm which automatically detect lung nodule candidates using CAD system. The system was applied to the helical CT images of 450 subjects. Currently, we are carrying out the clinical field test program using the CAD system. The results of our CAD system have indicated good performance when compared with physician's diagnosis. The experimental results of the algorithm indicate that our CAD system is useful to increase the efficiency of the mass screening process. CT screening of thorax will be performed by using the CAD system as a counterpart to the double reading technique actually used in herical CT screening program, not by using the film display.

  8. Computer-aided diagnosis system for lung cancer based on retrospective helical CT images

    NASA Astrophysics Data System (ADS)

    Satoh, Hitoshi; Ukai, Yuji; Niki, Noboru; Eguchi, Kenji; Mori, Kiyoshi; Ohmatsu, Hironobu; Kakinuma, Ryutaro; Kaneko, Masahiro; Moriyama, Noriyuki

    1999-05-01

    In this paper, we present a computer-aided diagnosis (CAD) system for lung cancer to detect nodule candidates at an early stage from the present and the early helical CT screening of the thorax. We developed an algorithm that can compare automatically the slice images of present and early CT scans for the assistance of comparative reading in retrospect. The algorithm consists of the ROI detection and shape analysis based on comparison of each slice image in the present and the early CT scans. The slice images of present and early CT scans are both displayed in parallel and analyzed quantitatively in order to detect the changes in size and intensity affection. We validated the efficiency of this algorithm by application to image data for mass screening of 50 subjects (total: 150 CT scans). The algorithm could compare the slice images correctly in most combinations with respect to physician's point of view. We validated the efficiency of the algorithm which automatically detect lung nodule candidates using CAD system. The system was applied to the helical CT images of 450 subjects. Currently, we are carrying out the clinical field test program using the CAD system. The results of our CAD system have indicated good performance when compared with physician's diagnosis. The experimental results of the algorithm indicate that our CAD system is useful to increase the efficiency of the mass screening process. CT screening of thorax will be performed by using the CAD system as a counterpart to the double reading technique actually used in herical CT screening program, not by using the film display.

  9. Role of Liquid-based Cytology and Cell Block in the Diagnosis of Endometrial Lesions

    PubMed Central

    Zhang, Hui; Wen, Jia; Xu, Pi-Li; Chen, Rui; Yang, Xi; Zhou, Lian-Er; Jiang, Ping; Wan, An-Xia; Liao, Qin-Ping

    2016-01-01

    Background: Liquid-based cytology (LBC) offers an alternative method to biopsy in screening endometrial cancer. Cell block (CB), prepared by collecting residual cytological specimen, represents a novel method to supplement the diagnosis of endometrial cytology. This study aimed to compare the specimen adequacy and diagnostic accuracy of LBC and CB in the diagnosis of endometrial lesions. Methods: A total of 198 women with high risks of endometrial carcinoma (EC) from May 2014 to April 2015 were enrolled in this study. The cytological specimens were collected by the endometrial sampler (SAP-1) followed by histopathologic evaluation of dilatation and curettage or biopsy guided by hysteroscopy. The residual cytological specimens were processed into paraffin-embedded CB after LBC preparation. Diagnostic accuracies of LBC and CB for detecting endometrial lesions were correlated with histological diagnoses. Chi-square test was used to compare the specimen adequacies of LBC and CB. Results: The specimen inadequate rate of CB was significantly higher than that of LBC (22.2% versus 7.1%, P < 0.01). There were 144 cases with adequate specimens for LBC and CB preparation. Among them, 29 cases were atypical endometrial hyperplasia (11 cases) or carcinoma (18 cases) confirmed by histology evaluation. Taking atypical hyperplasia and carcinoma as positive, the diagnostic accuracy of CB was 95.1% while it was 93.8% in LBC. When combined LBC with CB, the diagnostic accuracy was improved to 95.8%, with a sensitivity of 89.7% and specificity of 97.4%. Conclusions: CB is a feasible and reproducible adjuvant method for screening endometrial lesions. A combination of CB and LBC can improve the diagnostic accuracy of endometrial lesions. PMID:27270542

  10. Diagnosis of cervical cells based on fractal and Euclidian geometrical measurements: Intrinsic Geometric Cellular Organization

    PubMed Central

    2014-01-01

    Background Fractal geometry has been the basis for the development of a diagnosis of preneoplastic and neoplastic cells that clears up the undetermination of the atypical squamous cells of undetermined significance (ASCUS). Methods Pictures of 40 cervix cytology samples diagnosed with conventional parameters were taken. A blind study was developed in which the clinic diagnosis of 10 normal cells, 10 ASCUS, 10 L-SIL and 10 H-SIL was masked. Cellular nucleus and cytoplasm were evaluated in the generalized Box-Counting space, calculating the fractal dimension and number of spaces occupied by the frontier of each object. Further, number of pixels occupied by surface of each object was calculated. Later, the mathematical features of the measures were studied to establish differences or equalities useful for diagnostic application. Finally, the sensibility, specificity, negative likelihood ratio and diagnostic concordance with Kappa coefficient were calculated. Results Simultaneous measures of the nuclear surface and the subtraction between the boundaries of cytoplasm and nucleus, lead to differentiate normality, L-SIL and H-SIL. Normality shows values less than or equal to 735 in nucleus surface and values greater or equal to 161 in cytoplasm-nucleus subtraction. L-SIL cells exhibit a nucleus surface with values greater than or equal to 972 and a subtraction between nucleus-cytoplasm higher to 130. L-SIL cells show cytoplasm-nucleus values less than 120. The rank between 120–130 in cytoplasm-nucleus subtraction corresponds to evolution between L-SIL and H-SIL. Sensibility and specificity values were 100%, the negative likelihood ratio was zero and Kappa coefficient was equal to 1. Conclusions A new diagnostic methodology of clinic applicability was developed based on fractal and euclidean geometry, which is useful for evaluation of cervix cytology. PMID:24742118

  11. The Program for climate Model diagnosis and Intercomparison: 20-th anniversary Symposium

    SciTech Connect

    Potter, Gerald L; Bader, David C; Riches, Michael; Bamzai, Anjuli; Joseph, Renu

    2011-01-05

    Twenty years ago, W. Lawrence (Larry) Gates approached the U.S. Department of Energy (DOE) Office of Energy Research (now the Office of Science) with a plan to coordinate the comparison and documentation of climate model differences. This effort would help improve our understanding of climate change through a systematic approach to model intercomparison. Early attempts at comparing results showed a surprisingly large range in control climate from such parameters as cloud cover, precipitation, and even atmospheric temperature. The DOE agreed to fund the effort at the Lawrence Livermore National Laboratory (LLNL), in part because of the existing computing environment and because of a preexisting atmospheric science group that contained a wide variety of expertise. The project was named the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and it has changed the international landscape of climate modeling over the past 20 years. In spring 2009 the DOE hosted a 1-day symposium to celebrate the twentieth anniversary of PCMDI and to honor its founder, Larry Gates. Through their personal experiences, the morning presenters painted an image of climate science in the 1970s and 1980s, that generated early support from the international community for model intercomparison, thereby bringing PCMDI into existence. Four talks covered Gates's early contributions to climate research at the University of California, Los Angeles (UCLA), the RAND Corporation, and Oregon State University through the founding of PCMDI to coordinate the Atmospheric Model Intercomparison Project (AMIP). The speakers were, in order of presentation, Warren Washington [National Center for Atmospheric Research (NCAR)], Kelly Redmond (Western Regional Climate Center), George Boer (Canadian Centre for Climate Modelling and Analysis), and Lennart Bengtsson [University of Reading, former director of the European Centre for Medium-Range Weather Forecasts (ECMWF)]. The afternoon session emphasized

  12. Multi-Atlas Based Representations for Alzheimer’s Disease Diagnosis

    PubMed Central

    Min, Rui; Wu, Guorong; Cheng, Jian; Wang, Qian; Shen, Dinggang

    2014-01-01

    Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods. PMID:24753060

  13. Development of an Anti-Elicitin Antibody-Based Immunohistochemical Assay for Diagnosis of Pythiosis.

    PubMed

    Inkomlue, Ruchuros; Larbcharoensub, Noppadol; Karnsombut, Patcharee; Lerksuthirat, Tassanee; Aroonroch, Rangsima; Lohnoo, Tassanee; Yingyong, Wanta; Santanirand, Pitak; Sansopha, Lalana; Krajaejun, Theerapong

    2016-01-01

    Pythiosis is an emerging and life-threatening infectious disease of humans and animals living in tropical and subtropical countries and is caused by the fungus-like organism Pythium insidiosum. Antifungals are ineffective against this pathogen. Most patients undergo surgical removal of the infected organ, and many die from advanced infections. Early and accurate diagnosis leads to prompt management and promotes better prognosis for affected patients. Immunohistochemical assays (IHCs) have been developed using rabbit antibodies raised against P. insidiosum crude extract, i.e., culture filtrate antigen (CFA), for the histodiagnosis of pythiosis, but cross-reactivity with pathogenic fungi compromises the diagnostic performance of the IHC. Therefore, there is a need to improve detection specificity. Recently, the elicitin protein, ELI025, was identified in P. insidiosum, but it was not identified in other human pathogens, including true fungi. The ELI025-encoding gene was successfully cloned and expressed as a recombinant protein in Escherichia coli. This study aims to develop a new IHC using the rabbit anti-ELI025 antibody (anti-ELI) and to compare its performance with the previously reported anti-CFA-based IHC. Thirty-eight P. insidiosum histological sections stained positive by anti-ELI-based and anti-CFA-based IHCs indicating 100% detection sensitivity for the two assays. The anti-ELI antibody stained negative for all 49 negative-control sections indicating 100% detection specificity. In contrast, the anti-CFA antibody stained positive for one of the 49 negative controls (a slide prepared from Fusarium-infected tissue) indicating 98% detection specificity. In conclusion, the anti-ELI based IHC is sensitive and specific for the histodiagnosis of pythiosis and is an improvement over the anti-CFA-based assay. PMID:26719582

  14. Development of an Anti-Elicitin Antibody-Based Immunohistochemical Assay for Diagnosis of Pythiosis

    PubMed Central

    Inkomlue, Ruchuros; Larbcharoensub, Noppadol; Karnsombut, Patcharee; Lerksuthirat, Tassanee; Aroonroch, Rangsima; Lohnoo, Tassanee; Yingyong, Wanta; Santanirand, Pitak; Sansopha, Lalana

    2015-01-01

    Pythiosis is an emerging and life-threatening infectious disease of humans and animals living in tropical and subtropical countries and is caused by the fungus-like organism Pythium insidiosum. Antifungals are ineffective against this pathogen. Most patients undergo surgical removal of the infected organ, and many die from advanced infections. Early and accurate diagnosis leads to prompt management and promotes better prognosis for affected patients. Immunohistochemical assays (IHCs) have been developed using rabbit antibodies raised against P. insidiosum crude extract, i.e., culture filtrate antigen (CFA), for the histodiagnosis of pythiosis, but cross-reactivity with pathogenic fungi compromises the diagnostic performance of the IHC. Therefore, there is a need to improve detection specificity. Recently, the elicitin protein, ELI025, was identified in P. insidiosum, but it was not identified in other human pathogens, including true fungi. The ELI025-encoding gene was successfully cloned and expressed as a recombinant protein in Escherichia coli. This study aims to develop a new IHC using the rabbit anti-ELI025 antibody (anti-ELI) and to compare its performance with the previously reported anti-CFA-based IHC. Thirty-eight P. insidiosum histological sections stained positive by anti-ELI-based and anti-CFA-based IHCs indicating 100% detection sensitivity for the two assays. The anti-ELI antibody stained negative for all 49 negative-control sections indicating 100% detection specificity. In contrast, the anti-CFA antibody stained positive for one of the 49 negative controls (a slide prepared from Fusarium-infected tissue) indicating 98% detection specificity. In conclusion, the anti-ELI based IHC is sensitive and specific for the histodiagnosis of pythiosis and is an improvement over the anti-CFA-based assay. PMID:26719582

  15. Evaluating the Wald Test for Item-Level Comparison of Saturated and Reduced Models in Cognitive Diagnosis

    ERIC Educational Resources Information Center

    de la Torre, Jimmy; Lee, Young-Sun

    2013-01-01

    This article used the Wald test to evaluate the item-level fit of a saturated cognitive diagnosis model (CDM) relative to the fits of the reduced models it subsumes. A simulation study was carried out to examine the Type I error and power of the Wald test in the context of the G-DINA model. Results show that when the sample size is small and a…

  16. Diagnosis support system based on clinical guidelines: comparison between case-based fuzzy cognitive maps and Bayesian networks.

    PubMed

    Douali, Nassim; Csaba, Huszka; De Roo, Jos; Papageorgiou, Elpiniki I; Jaulent, Marie-Christine

    2014-01-01

    Several studies have described the prevalence and severity of diagnostic errors. Diagnostic errors can arise from cognitive, training, educational and other issues. Examples of cognitive issues include flawed reasoning, incomplete knowledge, faulty information gathering or interpretation, and inappropriate use of decision-making heuristics. We describe a new approach, case-based fuzzy cognitive maps, for medical diagnosis and evaluate it by comparison with Bayesian belief networks. We created a semantic web framework that supports the two reasoning methods. We used database of 174 anonymous patients from several European hospitals: 80 of the patients were female and 94 male with an average age 45±16 (average±stdev). Thirty of the 80 female patients were pregnant. For each patient, signs/symptoms/observables/age/sex were taken into account by the system. We used a statistical approach to compare the two methods.

  17. Improving Clinical Risk Stratification at Diagnosis in Primary Prostate Cancer: A Prognostic Modelling Study

    PubMed Central

    Wright, Karen A.; Muir, Kenneth R.; Gavin, Anna

    2016-01-01

    . Using this approach, a new five-stratum risk stratification system was produced, and its prognostic power was compared against the current system, with PCSM as the outcome. The results were analysed using a Cox hazards model, the log-rank test, Kaplan-Meier curves, competing-risks regression, and concordance indices. In the training set, the new risk stratification system identified distinct subgroups with different risks of PCSM in pair-wise comparison (p < 0.0001). Specifically, the new classification identified a very low-risk group (Group 1), a subgroup of intermediate-risk cancers with a low PCSM risk (Group 2, hazard ratio [HR] 1.62 [95% CI 0.96–2.75]), and a subgroup of intermediate-risk cancers with an increased PCSM risk (Group 3, HR 3.35 [95% CI 2.04–5.49]) (p < 0.0001). High-risk cancers were also sub-classified by the new system into subgroups with lower and higher PCSM risk: Group 4 (HR 5.03 [95% CI 3.25–7.80]) and Group 5 (HR 17.28 [95% CI 11.2–26.67]) (p < 0.0001), respectively. These results were recapitulated in the testing set and remained robust after inclusion of competing risks. In comparison to the current risk stratification system, the new system demonstrated improved prognostic performance, with a concordance index of 0.75 (95% CI 0.72–0.77) versus 0.69 (95% CI 0.66–0.71) (p < 0.0001). In an external cohort, the new system achieved a concordance index of 0.79 (95% CI 0.75–0.84) for predicting PCSM versus 0.66 (95% CI 0.63–0.69) (p < 0.0001) for the current NICE risk stratification system. The main limitations of the study were that it was registry based and that follow-up was relatively short. Conclusions A novel and simple five-stratum risk stratification system outperforms the standard three-stratum risk stratification system in predicting the risk of PCSM at diagnosis in men with primary non-metastatic prostate cancer, even when accounting for competing risks. This model also allows delineation of new clinically relevant

  18. Improving Clinical Risk Stratification at Diagnosis in Primary Prostate Cancer: A Prognostic Modelling Study

    PubMed Central

    Wright, Karen A.; Muir, Kenneth R.; Gavin, Anna

    2016-01-01

    . Using this approach, a new five-stratum risk stratification system was produced, and its prognostic power was compared against the current system, with PCSM as the outcome. The results were analysed using a Cox hazards model, the log-rank test, Kaplan-Meier curves, competing-risks regression, and concordance indices. In the training set, the new risk stratification system identified distinct subgroups with different risks of PCSM in pair-wise comparison (p < 0.0001). Specifically, the new classification identified a very low-risk group (Group 1), a subgroup of intermediate-risk cancers with a low PCSM risk (Group 2, hazard ratio [HR] 1.62 [95% CI 0.96–2.75]), and a subgroup of intermediate-risk cancers with an increased PCSM risk (Group 3, HR 3.35 [95% CI 2.04–5.49]) (p < 0.0001). High-risk cancers were also sub-classified by the new system into subgroups with lower and higher PCSM risk: Group 4 (HR 5.03 [95% CI 3.25–7.80]) and Group 5 (HR 17.28 [95% CI 11.2–26.67]) (p < 0.0001), respectively. These results were recapitulated in the testing set and remained robust after inclusion of competing risks. In comparison to the current risk stratification system, the new system demonstrated improved prognostic performance, with a concordance index of 0.75 (95% CI 0.72–0.77) versus 0.69 (95% CI 0.66–0.71) (p < 0.0001). In an external cohort, the new system achieved a concordance index of 0.79 (95% CI 0.75–0.84) for predicting PCSM versus 0.66 (95% CI 0.63–0.69) (p < 0.0001) for the current NICE risk stratification system. The main limitations of the study were that it was registry based and that follow-up was relatively short. Conclusions A novel and simple five-stratum risk stratification system outperforms the standard three-stratum risk stratification system in predicting the risk of PCSM at diagnosis in men with primary non-metastatic prostate cancer, even when accounting for competing risks. This model also allows delineation of new clinically relevant

  19. Equipment fault diagnosis system of sequencing batch reactors using rule-based fuzzy inference and on-line sensing data.

    PubMed

    Kim, Y J; Bae, H; Poo, K M; Ko, J H; Kim, B G; Park, T J; Kim, C W

    2006-01-01

    The importance of a detection technique to prevent process deterioration is increasing. For the fast detection of this disturbance, a diagnostic algorithm was developed to determine types of equipment faults by using on-line ORP and DO profile in sequencing batch reactors (SBRs). To develop the rule base for fault diagnosis, the sensor profiles were obtained from a pilot-scale SBR when blower, influent pump and mixer were broken. The rules were generated based on the calculated error between an abnormal profile and a normal profile, e(ORP)(t) and e(DO)(t). To provide intermediate diagnostic results between "normal" and "fault", a fuzzy inference algorithm was incorporated to the rules. Fuzzified rules could present the diagnosis result "need to be checked". The diagnosis showed good performance in detecting and diagnosing various faults. The developed algorithm showed its applicability to detect faults and make possible fast action to correct them. PMID:16722090

  20. Model-based sensor location selection for helicopter gearbox monitoring

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Wang, Keming; Danai, Kourosh; Lewicki, David G.

    1996-01-01

    A new methodology is introduced to quantify the significance of accelerometer locations for fault diagnosis of helicopter gearboxes. The basis for this methodology is an influence model which represents the effect of various component faults on accelerometer readings. Based on this model, a set of selection indices are defined to characterize the diagnosability of each component, the coverage of each accelerometer, and the relative redundancy between the accelerometers. The effectiveness of these indices is evaluated experimentally by measurement-fault data obtained from an OH-58A main rotor gearbox. These data are used to obtain a ranking of individual accelerometers according to their significance in diagnosis. Comparison between the experimentally obtained rankings and those obtained from the selection indices indicates that the proposed methodology offers a systematic means for accelerometer location selection.

  1. PCR based diagnosis of trypanosomiasis exploring invariant surface glycoprotein (ISG) 75 gene.

    PubMed

    Rudramurthy, G R; Sengupta, P P; Balamurugan, V; Prabhudas, K; Rahman, H

    2013-03-31

    The invariant surface glycoprotein (ISG-75) gene of Trypanosoma evansi buffalo isolate from Karnataka state in India was sequenced and analyzed to elucidate its relationship with other isolates/species. The sequenced ISG-75 gene was also explored to device a polymerase chain reaction (PCR) strategy for the diagnosis of trypanosomiasis in carrier animals. The six cloned ISG gene sequences revealed the open reading frame (ORF) of 1572 and 1527 nucleotide (nt) encoding a polypeptide of 523 and 508 amino acids (aa) respectively and belongs ISG-75 gene family. Sequence analysis revealed 91-100% and 65-99% similarity at nt and aa levels, respectively with other isolates/species and belongs to the RoTat 1.2 strain. The diagnostic PCR based on ISG-75 sequence amplifies a 407 bp product specifically from the different T. evansi isolates and could detect 0.04 pg and 1.2 ng of DNA from purified trypanosomes and T. evansi infected rat blood samples respectively. Subsequently the PCR detected 0.02 and 0.27 trypanosomes ml(-1) respectively, from purified trypanosomes and T. evansi (buffalo isolate) infected rat blood. By the developed PCR assay trypanosomal nucleic acid was detected in experimental rats and buffalo on 24 h post infection (p.i.) and 3rd day post infection (d.p.i.), respectively. The developed ISG-75 gene based PCR assay could be useful in detection of carrier status of trypanosomiasis in animals.

  2. A ROC-based feature selection method for computer-aided detection and diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

    2014-03-01

    Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

  3. Research of singular value decomposition based on slip matrix for rolling bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Cong, Feiyun; Zhong, Wei; Tong, Shuiguang; Tang, Ning; Chen, Jin

    2015-05-01

    Rolling element bearings are at the heart of most rotating machines and they bear the function of connectivity between the rotor and stator. It is important to distinguish the incipient fault before the bearing step into serious failure. The Slip Matrix (SM) construction method based on Singular Value Decomposition (SVD) is proposed in this paper. The SM based fault feature extraction and impulses intelligent detection methods are introduced as the key steps for rolling bearing fault diagnosis. The numerical simulation of rolling bearing fault signal is adopted which shows that the proposed method is good at fault impulses detection in strong background noise environment. The rolling element bearing accelerated life test is performed for the acquisition of experimental data of rolling bearing fault. With the rolling bearing running from normal state to failure, the initial fault signal part can be picked out from the whole life vibration data of the rolling bearing. The vibration signal is close to the nature fault signal which is acquired from a rolling bearing applied in industrial field. The analysis result shows that the proposed method has an excellent performance in rolling bearing fault detection.

  4. Maximum margin classification based on flexible convex hulls for fault diagnosis of roller bearings

    NASA Astrophysics Data System (ADS)

    Zeng, Ming; Yang, Yu; Zheng, Jinde; Cheng, Junsheng

    2016-01-01

    A maximum margin classification based on flexible convex hulls (MMC-FCH) is proposed and applied to fault diagnosis of roller bearings. In this method, the class region of each sample set is approximated by a flexible convex hull of its training samples, and then an optimal separating hyper-plane that maximizes the geometric margin between flexible convex hulls is constructed by solving a closest pair of points problem. By using the kernel trick, MMC-FCH can be extended to nonlinear cases. In addition, multi-class classification problems can be processed by constructing binary pairwise classifiers as in support vector machine (SVM). Actually, the classical SVM also can be regarded as a maximum margin classification based on convex hulls (MMC-CH), which approximates each class region with a convex hull. The convex hull is a special case of the flexible convex hull. To train a MMC-FCH classifier, time-domain and frequency-domain statistical parameters are extracted not only from raw vibration signals but also from the resulting intrinsic mode functions (IMFs) by performing empirical mode decomposition (EMD) on the raw signals, and then the distance evaluation technique (DET) is used to select salient features from the whole statistical features. The experiments on bearing datasets show that the proposed method can reliably recognize different bearing faults.

  5. ENVIRONMENTAL AUDITING: A Vegetation-Based Method for Ecological Diagnosis of Riverine Wetlands.

    PubMed

    Amoros; Bornette; Henry

    2000-02-01

    / The management of riverine wetlands, recognized as a major component of biodiversity in fluvial hydrosystems, is problematic. Preservation or restoration of such ecosystems requires a method to assess the major ecological processes operating in the wetlands, the sustainability of the aquatic stage, and the restoration potential of each riverine wetland. We propose a method of diagnosis based on aquatic macrophytes and helophytes. Plant communities are used because they are easy to survey and provide information about (1) the origin of a water supply (i.e., groundwater, seepage, or surface river water) and its nutrient content, (2) effects of flood disturbances, and (3) terrestrialization processes. The novelty of the method is that, in contrast to available typologies, it is based on the interference of gradients resulting from several processes, which makes it possible to predict wetland sustainability and restoration potential. These predictions result from knowledge of the processes involved in terrestrialization, i.e., the influence of flood disturbances, occurrence of groundwater supplies, trophic degree, and water permanency of the habitat during a yearly cycle. The method is demonstrated on five different river systems.

  6. Model-based tomographic reconstruction

    DOEpatents

    Chambers, David H.; Lehman, Sean K.; Goodman, Dennis M.

    2012-06-26

    A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.

  7. Energy based hybrid turbulence modeling

    NASA Astrophysics Data System (ADS)

    Haering, Sigfried; Moser, Robert

    2015-11-01

    Traditional hybrid approaches exhibit deficiencies when used for fluctuating smooth-wall separation and reattachment necessitating ad-hoc delaying functions and model tuning making them no longer useful as a predictive tool. Additionally, complex geometries and flows often require high cell aspect-ratios and large grid gradients as a compromise between resolution and cost. Such transitions and inconsistencies in resolution detrimentally effect the fidelity of the simulation. We present the continued development of a new hybrid RANS/LES modeling approach specifically developed to address these challenges. In general, modeled turbulence is returned to resolved scales by reduced or negative model viscosity until a balance between theoretical and actual modeled turbulent kinetic energy is attained provided the available resolution. Anisotropy in the grid and resolved field are directly integrated into this balance. A viscosity-based correction is proposed to account for resolution inhomogeneities. Both the hybrid framework and resolution gradient corrections are energy conserving through an exchange of resolved and modeled turbulence.

  8. Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples

    NASA Astrophysics Data System (ADS)

    Zhang, Meijun; Tang, Jian; Zhang, Xiaoming; Zhang, Jiaojiao

    2016-03-01

    The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD(empirical mode decomposition) and EEMD(ensemble empirical mode decomposition) are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM(support vector machine), extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD+SVM method is compared with the EEMD+SVM and EMD+SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

  9. A gearbox fault diagnosis scheme based on near-field acoustic holography and spatial distribution features of sound field

    NASA Astrophysics Data System (ADS)

    Lu, Wenbo; Jiang, Weikang; Yuan, Guoqing; Yan, Li

    2013-05-01

    Vibration signal analysis is the main technique in machine condition monitoring or fault diagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for gearbox based on near-field acoustic holography (NAH) and spatial distribution features of sound field is presented in this paper. It focuses on applying distribution information of sound field to gearbox fault diagnosis. A two-stage industrial helical gearbox is experimentally studied in a semi-anechoic chamber and a lab workshop, respectively. Firstly, multi-class faults (mild pitting, moderate pitting, severe pitting and tooth breakage) are simulated, respectively. Secondly, sound fields and corresponding acoustic images in different gearbox running conditions are obtained by fast Fourier transform (FFT) based NAH. Thirdly, by introducing texture analysis to fault diagnosis, spatial distribution features are extracted from acoustic images for capturing fault patterns underlying the sound field. Finally, the features are fed into multi-class support vector machine for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with traditional ABD method. Even with strong noise interference, spatial distribution features of sound field can reliably reveal the fault patterns of gearbox, and thus the satisfactory accuracy can be obtained. The combination of histogram features and gray level gradient co-occurrence matrix features is suggested for good diagnosis accuracy and low time cost.

  10. Double-hairpin molecular-beacon-based amplification detection for gene diagnosis linked to cancer.

    PubMed

    Xu, Huo; Zhang, Rongbo; Li, Feng; Zhou, Yingying; Peng, Ting; Wang, Xuedong; Shen, Zhifa

    2016-09-01

    A powerful double-hairpin molecular beacon (DHMB) was developed for cancer-related KRAS gene detection based on the one-to-two stoichiometry. During target DNA detection, DHMB can execute signal transduction even if no any exogenous element is involved. Unlike the conventional molecular beacon based on the one-to-one interaction, one target DNA not only hybridizes with one DHMB and opens its hairpin but also promotes the interaction between two DHMBs, causing the separation of two fluorophores from quenchers. This leads to an enhanced fluorescence signal. As a result, the target KRAS gene is able to be detected within a wide dynamic range from 0.05 to 200 nM with the detection limit of 50 pM, indicating a dramatic improvement compared with traditional molecular beacons. Moreover, the point mutations existing in target DNAs can be easily screened. The potential application for target species in real samples was indicated by the analysis of PCR amplicons of DNAs from the DNA extracted from SW620 cell. Besides becoming a promising candidate probe for molecular biology research and clinical diagnosis of genetic diseases, the DHMB is expected to provide a significant insight into the design of DNA probe-based homogenous sensing systems. Graphical Abstract A powerful double-hairpin molecular beacon (DHMB) was developed for cancer-related gene KRAS detection based on the one-to-two stoichiometry. Without the help of any exogenous probe, the point mutation is easily screened, and the target DNA can be quantified down to 50 pM, indicating a dramatic improvement compared with traditional molecular beacons. PMID:27422649

  11. Double-hairpin molecular-beacon-based amplification detection for gene diagnosis linked to cancer.

    PubMed

    Xu, Huo; Zhang, Rongbo; Li, Feng; Zhou, Yingying; Peng, Ting; Wang, Xuedong; Shen, Zhifa

    2016-09-01

    A powerful double-hairpin molecular beacon (DHMB) was developed for cancer-related KRAS gene detection based on the one-to-two stoichiometry. During target DNA detection, DHMB can execute signal transduction even if no any exogenous element is involved. Unlike the conventional molecular beacon based on the one-to-one interaction, one target DNA not only hybridizes with one DHMB and opens its hairpin but also promotes the interaction between two DHMBs, causing the separation of two fluorophores from quenchers. This leads to an enhanced fluorescence signal. As a result, the target KRAS gene is able to be detected within a wide dynamic range from 0.05 to 200 nM with the detection limit of 50 pM, indicating a dramatic improvement compared with traditional molecular beacons. Moreover, the point mutations existing in target DNAs can be easily screened. The potential application for target species in real samples was indicated by the analysis of PCR amplicons of DNAs from the DNA extracted from SW620 cell. Besides becoming a promising candidate probe for molecular biology research and clinical diagnosis of genetic diseases, the DHMB is expected to provide a significant insight into the design of DNA probe-based homogenous sensing systems. Graphical Abstract A powerful double-hairpin molecular beacon (DHMB) was developed for cancer-related gene KRAS detection based on the one-to-two stoichiometry. Without the help of any exogenous probe, the point mutation is easily screened, and the target DNA can be quantified down to 50 pM, indicating a dramatic improvement compared with traditional molecular beacons.

  12. Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

    PubMed

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

    Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient. PMID:23112645

  13. Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

    PubMed Central

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

    Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient. PMID:23112645

  14. Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

    PubMed

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

    Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

  15. Subclass-based multi-task learning for Alzheimer's disease diagnosis

    PubMed Central

    Suk, Heung-II; Lee, Seong-Whan; Shen, Dinggang

    2014-01-01

    In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak1 distribution of classes. The rationale for our approach is that it is highly likely for neuroimaging data to have multiple peaks or modes in distribution, e.g., mixture of Gaussians, due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak in the underlying multipeak distribution. Specifically, after performing clustering for each class, we encode the respective subclasses, i.e., clusters, with their unique codes. In encoding, we impose the subclasses of the same original class close to each other and those of different original classes distinct from each other. By setting the codes as new label vectors of our training samples, we formulate a multi-task learning problem in a ℓ2,1-penalized regression framework, through which we finally select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by improving the classification accuracies by 1% (AD vs. Normal Control: NC), 3.25% (MCI vs. NC), 5.34% (AD vs. MCI), and 7.4% (MCI Converter: MCI-C vs. MCI Non-Converter: MCI-NC) compared to the competing single-task learning method. It is remarkable for the performance improvement in MCI-C vs. MCI-NC classification, which is the most important for early diagnosis and treatment. It is also noteworthy that with the strategy of modality-adaptive weights by means of a multi-kernel support vector machine, we maximally achieved the classification accuracies of 96.18% (AD vs. NC), 81.45% (MCI vs. NC), 73.21% (AD vs. MCI), and

  16. Subclass-based multi-task learning for Alzheimer's disease diagnosis.

    PubMed

    Suk, Heung-Ii; Lee, Seong-Whan; Shen, Dinggang

    2014-01-01

    In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak distribution of classes. The rationale for our approach is that it is highly likely for neuroimaging data to have multiple peaks or modes in distribution, e.g., mixture of Gaussians, due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak in the underlying multipeak distribution. Specifically, after performing clustering for each class, we encode the respective subclasses, i.e., clusters, with their unique codes. In encoding, we impose the subclasses of the same original class close to each other and those of different original classes distinct from each other. By setting the codes as new label vectors of our training samples, we formulate a multi-task learning problem in a ℓ2,1-penalized regression framework, through which we finally select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by improving the classification accuracies by 1% (AD vs. Normal Control: NC), 3.25% (MCI vs. NC), 5.34% (AD vs. MCI), and 7.4% (MCI Converter: MCI-C vs. MCI Non-Converter: MCI-NC) compared to the competing single-task learning method. It is remarkable for the performance improvement in MCI-C vs. MCI-NC classification, which is the most important for early diagnosis and treatment. It is also noteworthy that with the strategy of modality-adaptive weights by means of a multi-kernel support vector machine, we maximally achieved the classification accuracies of 96.18% (AD vs. NC), 81.45% (MCI vs. NC), 73.21% (AD vs. MCI), and 74

  17. Inherent Structure Based Multi-view Learning with Multi-template Feature Representation for Alzheimer’s Disease Diagnosis

    PubMed Central

    Liu, Mingxia; Zhang, Daoqiang; Adeli-Mosabbeb, Ehsan; Shen, Dinggang

    2016-01-01

    Multi-template based brain morphometric pattern analysis using magnetic resonance imaging (MRI) has been recently proposed for automatic diagnosis of Alzheimer’s disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multi-view morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multi-template based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality the underlying data distribution is actually not pre-known. In this paper, we propose an inherent structure based multi-view leaning (ISML) method using multiple templates for AD/MCI classification. Specifically, we first extract multi-view feature representations for subjects using multiple selected templates, and then cluster subjects within a specific class into several sub-classes (i.e., clusters) in each view space. Then, we encode those sub-classes with unique codes by considering both their original class information and their own distribution information, followed by a multi-task feature selection model. Finally, we learn an ensemble of view-specific support vector machine (SVM) classifiers based on their respectively selected features in each view, and fuse their results to draw the final decision. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multi-template based methods. PMID:26540666

  18. Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

    NASA Astrophysics Data System (ADS)

    Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Peng, Zhongxiao; Li, Li

    2011-10-01

    Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and

  19. Verification of Parent-Report of Child Autism Spectrum Disorder Diagnosis to a Web-Based Autism Registry

    ERIC Educational Resources Information Center

    Daniels, Amy M.; Rosenberg, Rebecca E.; Anderson, Connie; Law, J. Kiely; Marvin, Alison R.; Law, Paul A.

    2012-01-01

    Growing interest in autism spectrum disorder (ASD) research requires increasingly large samples to uncover epidemiologic trends; such a large dataset is available in a national, web-based autism registry, the Interactive Autism Network (IAN). The objective of this study was to verify parent-report of professional ASD diagnosis to the registry's…

  20. Sex disparities in diagnosis of bladder cancer after initial presentation with hematuria: a nationwide claims-based investigation

    PubMed Central

    Cohn, Joshua A.; Vekhter, Benjamin; Lyttle, Christopher; Steinberg, Gary D.; Large, Michael C.

    2013-01-01

    Background Women have disproportionately higher mortality rates relative to incidence for bladder cancer. Multiple etiologies have been proposed, including delayed diagnosis and treatment. Guidelines recommend rule-out of malignancy in men and women presenting with hematuria. We aimed to determine the difference in timing from presentation with hematuria to diagnosis of bladder cancer in women versus men. Methods This is a retrospective population-based study examining the timing from presentation with hematuria to diagnosis of bladder cancer, based on data from the MarketScan databases, which include enrollees of more than 100 health insurances plans of approximately 40 large US employers from 2004 through 2010. All study patients presented with hematuria and were subsequently diagnosed with bladder cancer. The primary outcome measure was number of days between initial presentation with hematuria and diagnosis of bladder cancer by gender. Results 5416 men and 2233 women met inclusion criteria. Mean days from initial hematuria claim to bladder cancer claim was significantly longer in women (85.4 vs. 73.6 days, p<0.001), and the proportion of women with >6 month delays in bladder cancer diagnosis significantly higher (17.3% vs. 14.1%, p<0.001). Women were more likely to be diagnosed with urinary tract infection (OR 2.32 [95% CI 2.07–2.59]) and less likely to undergo abdominal or pelvic imaging (OR 0.80 [95% CI 0.71–0.89]). Conclusions Both men and women experience significant delays between presentation with hematuria and diagnosis of bladder cancer, with longer delays for women. This may be partly responsible for the gender-based discrepancy in outcomes associated with bladder cancer. PMID:24496869

  1. Lipopolysaccharide Specific Immunochromatography Based Lateral Flow Assay for Serogroup Specific Diagnosis of Leptospirosis in India

    PubMed Central

    Vanithamani, Shanmugam; Shanmughapriya, Santhanam; Narayanan, Ramasamy; Raja, Veerapandian; Kanagavel, Murugesan; Sivasankari, Karikalacholan; Natarajaseenivasan, Kalimuthusamy

    2015-01-01

    Background Leptospirosis is a re-emerging infectious disease that is under-recognized due to low-sensitivity and cumbersome serological tests. MAT is the gold standard test and it is the only serogroup specific test used till date. Rapid reliable alternative serogroup specific tests are needed for surveillance studies to identify locally circulating serogroups in the study area. Methods/Principal Findings In the present investigation the serological specificity of leptospiral lipopolysaccharides (LPS) was evaluated by enzyme linked immunosorbent assay (ELISA), dot blot assay and rapid immunochromatography based lateral flow assay (ICG-LFA). Sera samples from 120 MAT positive cases, 174 cases with febrile illness other than leptospirosis, and 121 seronegative healthy controls were evaluated for the diagnostic sensitivity and specificity of the developed assays. LPS was extracted from five locally predominant circulating serogroups including: Australis (27.5%), Autumnalis (11.7%), Ballum (25.8%), Grippotyphosa (12.5%), Pomona (10%) and were used as antigens in the diagnostics to detect IgM antibodies in patients’ sera. The sensitivity observed by IgM ELISA and dot blot assay using various leptospiral LPS was >90% for homologous sera. Except for Ballum LPS, no other LPS showed cross-reactivity to heterologous sera. An attempt was made to develop LPS based ICG-LFA for rapid and sensitive serogroup specific diagnostics of leptospirosis. The developed ICG-LFA showed sensitivity in the range between 93 and 100% for homologous sera. The Wilcoxon analysis showed LPS based ICG-LFA did not differ significantly from the gold standard MAT (P>0.05). Conclusion The application of single array of LPS for serogroup specific diagnosis is first of its kind. The developed assay could potentially be evaluated and employed for as MAT alternative. PMID:26340095

  2. Biofunctionalized Silica Nanoparticles: Standards in Amyloid-β Oligomer-Based Diagnosis of Alzheimer's Disease.

    PubMed

    Hülsemann, Maren; Zafiu, Christian; Kühbach, Katja; Lühmann, Nicole; Herrmann, Yvonne; Peters, Luriano; Linnartz, Christina; Willbold, Johannes; Kravchenko, Kateryna; Kulawik, Andreas; Willbold, Sabine; Bannach, Oliver; Willbold, Dieter

    2016-07-27

    Amyloid-β (Aβ) oligomers represent a promising biomarker for the early diagnosis of Alzheimer's disease (AD). However, state-of-the-art methods for immunodetection of Aβ oligomers in body fluids show a large variability and lack a reliable and stable standard that enables the reproducible quantitation of Aβ oligomers. At present, the only available standard applied in these assays is based on a random aggregation process of synthetic Aβ and has neither a defined size nor a known number of epitopes. In this report, we generated a highly stable standard in the size range of native Aβ oligomers that exposes a defined number of epitopes. The standard consists of a silica nanoparticle (SiNaP), which is functionalized with Aβ peptides on its surface (Aβ-SiNaP). The different steps of Aβ-SiNaP synthesis were followed by microscopic, spectroscopic and biochemical analyses. To investigate the performance of Aβ-SiNaPs as an appropriate standard in Aβ oligomer immunodetection, Aβ-SiNaPs were diluted in cerebrospinal fluid and quantified down to a concentration of 10 fM in the sFIDA (surface-based fluorescence intensity distribution analysis) assay. This detection limit corresponds to an Aβ concentration of 1.9 ng l-1 and lies in the sensitivity range of currently applied diagnostic tools based on Aβ oligomer quantitation. Thus, we developed a highly stable and well-characterized standard for the application in Aβ oligomer immunodetection assays that finally allows the reproducible quantitation of Aβ oligomers down to single molecule level and provides a fundamental improvement for the worldwide standardization process of diagnostic methods in AD research. PMID:27472876

  3. Image-Based Predictive Modeling of Heart Mechanics.

    PubMed

    Wang, V Y; Nielsen, P M F; Nash, M P

    2015-01-01

    Personalized biophysical modeling of the heart is a useful approach for noninvasively analyzing and predicting in vivo cardiac mechanics. Three main developments support this style of analysis: state-of-the-art cardiac imaging technologies, modern computational infrastructure, and advanced mathematical modeling techniques. In vivo measurements of cardiac structure and function can be integrated using sophisticated computational methods to investigate mechanisms of myocardial function and dysfunction, and can aid in clinical diagnosis and developing personalized treatment. In this article, we review the state-of-the-art in cardiac imaging modalities, model-based interpretation of 3D images of cardiac structure and function, and recent advances in modeling that allow personalized predictions of heart mechanics. We discuss how using such image-based modeling frameworks can increase the understanding of the fundamental biophysics behind cardiac mechanics, and assist with diagnosis, surgical guidance, and treatment planning. Addressing the challenges in this field will require a coordinated effort from both the clinical-imaging and modeling communities. We also discuss future directions that can be taken to bridge the gap between basic science and clinical translation.

  4. Crowdsourcing Based 3d Modeling

    NASA Astrophysics Data System (ADS)

    Somogyi, A.; Barsi, A.; Molnar, B.; Lovas, T.

    2016-06-01

    Web-based photo albums that support organizing and viewing the users' images are widely used. These services provide a convenient solution for storing, editing and sharing images. In many cases, the users attach geotags to the images in order to enable using them e.g. in location based applications on social networks. Our paper discusses a procedure that collects open access images from a site frequently visited by tourists. Geotagged pictures showing the image of a sight or tourist attraction are selected and processed in photogrammetric processing software that produces the 3D model of the captured object. For the particular investigation we selected three attractions in Budapest. To assess the geometrical accuracy, we used laser scanner and DSLR as well as smart phone photography to derive reference values to enable verifying the spatial model obtained from the web-album images. The investigation shows how detailed and accurate models could be derived applying photogrammetric processing software, simply by using images of the community, without visiting the site.

  5. Nanovesicle-based bioelectronic nose for the diagnosis of lung cancer from human blood.

    PubMed

    Lim, Jong Hyun; Park, Juhun; Oh, Eun Hae; Ko, Hwi Jin; Hong, Seunghun; Park, Tai Hyun

    2014-03-01

    A human nose-mimetic diagnosis system that can distinguish the odor of a lung cancer biomarker, heptanal, from human blood is presented. Selective recognition of the biomarker is mimicked in the human olfactory system. A specific olfactory receptor recognizing the chemical biomarker is first selected through screening a library of human olfactory receptors (hORs). The selected hOR is expressed on the membrane of human embryonic kidney (HEK)-293 cells. Nanovesicles containing the hOR on the membrane are produced from these cells, and are then used for the functionalization of single-walled carbon nanotubes. This strategy allows the development of a sensitive and selective nanovesicle-based bioelectronic nose (NvBN). The NvBN is able to selectively detect heptanal at a concentration as low as 1 × 10(-14) m, a sufficient level to distinguish the blood of a lung cancer patient from the blood of a healthy person. In actual experiments, NvBN could detect an extremely small increase in the amount of heptanal from human blood plasma without any pretreatment processes. This result offers a rapid and easy method to analyze chemical biomarkers from human blood in real-time and to diagnose lung cancer.

  6. Current status of nanoparticle-based imaging agents for early diagnosis of cancer and atherosclerosis.

    PubMed

    Saravanakumar, Gurusamy; Kim, Kwangmeyung; Park, Jae Hyung; Rhee, Kyehan; Kwon, Ick Chan

    2009-02-01

    Endothelium plays a vital role in various vascular functions, and its dysfunction is a key underlying process closely related to diverse array of diseases such as atherosclerosis and tumor. Therefore, early detection of endothelial dysfunction at the functional or molecular level is of high importance for effective therapy. Recent advances in nanotechnology and our understanding in cellular and molecular biology have provided various biomedical imaging modalities and nanosized multimodal imaging agents. Multimodal nanoparticles that encompass the targeting ligands and magnetic/optical imaging labels enable us to visualize the pathophysiological process of various diseases using multiple imaging techniques. To visualize the specific pathogenic process at the molecular level, the imaging probe needs to be surface-functionalized with specific affinity ligands such as monoclonal antibodies (mAb). Combining the functional imaging agents along with therapeutic drugs has great potential for effective early detection, accurate diagnosis, and treatment of disease. The current review will highlight the application of various nanoparticle-based imaging agents and their recent developments in diagnosing endothelium dysfunction with a special emphasis on atherosclerosis and cancer.

  7. Bearing fault diagnosis based on variational mode decomposition and total variation denoising

    NASA Astrophysics Data System (ADS)

    Zhang, Suofeng; Wang, Yanxue; He, Shuilong; Jiang, Zhansi

    2016-07-01

    Feature extraction plays an essential role in bearing fault detection. However, the measured vibration signals are complex and non-stationary in nature, and meanwhile impulsive signatures of rolling bearing are usually immersed in stochastic noise. Hence, a novel hybrid fault diagnosis approach is developed for the denoising and non-stationary feature extraction in this work, which combines well with the variational mode decomposition (VMD) and majoriation-minization based total variation denoising (TV-MM). The TV-MM approach is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the parameter λ is very important in TV-MM, the weighted kurtosis index is also proposed in this work to determine an appropriate λ used in TV-MM. The performance of the proposed hybrid approach is conducted through the analysis of the simulated and practical bearing vibration signals. Results demonstrate that the proposed approach has superior capability to detect roller bearing faults from vibration signals.

  8. A colorimetric aptasensor for the diagnosis of malaria based on cationic polymers and gold nanoparticles.

    PubMed

    Jeon, Weejeong; Lee, Seonghwan; Manjunatha, D H; Ban, Changill

    2013-08-01

    Malaria, a major burden of disease caused by parasites of the genus Plasmodium, is widely spread in tropical and subtropical regions. Here, we have successfully developed a diagnostic technique for malaria. The proposed method is based on the inte