Issues in practical model-based diagnosis

The model-based diagnosis project at the University of Twente has been directed at improving the practical usefulness of model-based diagnosis. In cooperation with industrial partners, the research addressed the modeling problem and the efficiency problem in model-based reasoning.Main results of this research are that (1) re-use of electronically available descriptions of systems for diagnostic purposes is possible, and (2) efficient reasoning can be realized using PDE, hierarchic models, and a simple diagnostic strategy. We have built a prototype diagnostic system which shows the technical feasibility of model-based diagnosis in a computer system.The project was concluded in July 1993 by the development of the 'Diagnostic Toolbox'. The Diagnostic Toolbox supports the modeling of technical systems for diagnostic purposes, and it contains several model-based reasoning methods

Computer aided diagnosis of cerebrovascular disease based on DSA image

In recent years, the incidence of cerebrovascular diseases in China has shown a significant upward trend, and it has become a common disease threatening people's lives. Digital Subtraction Angiography (DSA) is the gold standard for the diagnosis of clinical cerebrovascular disease, and it is the most direct method to check the brain lesion. At present, there are the following two problems in the clinical research of DSA images: DSA is a real-time image with numerous frames, containing much useless information in frames; thus, human interpretation and annotation are time-consuming and labor-intensive. The blood vessel structure in DSA images is so complicated that high practical skills are required for clinicians. In the computer-aided diagnosis of DSA sequence images, there is currently a lack of automatic and effective computer-aided diagnosis algorithms for cerebrovascular diseases. Based on the above issues, the main work of this paper is as follows: 1.A multi-target detection algorithm based on Faster-RCNN is designed and applied to the analysis of brain DSA images. The algorithm divides DSA images into arterial phase, capillary phase, pre-venous phase and sinus phase by identifying the main blood vessel structure in each frame. And on this basis, we analyze the time relationship between the time phases. 2.On the basis of DSA phase detection, a key frame location algorithm based on single blood vessel structure detection is designed for moyamoya disease. First, the target detection model is applied to locate the internal carotid artery and the Willis circle. Then, five frames of images are extracted from the arterial period as keyframes. Finally, the nidus' ROI is determined according to the position of the internal carotid artery. 3.A diagnostic method for cerebral arteriovenous malformation (AVM) is designed, which combines temporal features and radiomics features. First, on the basis of DSA time phase detection, we propose a deep learning network to extract vascular time features from the DSA video; then, the time feature is combined with the radiomics features of the static keyframe to establish an AVM diagnosis model. While assisting diagnosis, this method does not require any human intervention, and reduces the workload of clinicians. The diagnostic model that combines time features and radiomics features is applied to the study of AVM staging. The experimental results prove that the classification model trained by fusion features has better diagnostic performance than the model trained by either time features or radiomics features. Based on the above three parts, this paper establishes a cerebrovascular disease analysis framework based on radiomics method and deep learning. We introduce corresponding solutions for DSA automatic image reading, rapid diagnosis of moyamoya disease, and precise diagnosis of AVM. The method proposed in this paper has practical significance for assisting the diagnosis of cerebrovascular disease and reducing the burden of medical staff.Digital Subtraction Angiography(DSA), Radiomics analysis, Arteriovenous malformations, Moyamoya, Faster-RCNN, Temporal features, Fusion feature

다양한 질환 심각도 하에서 말초동맥 질환 위치 식별을 위한 딥러닝 기반 도메인 적응 방법 연구

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2022.2. 윤병동.This paper's primary purpose is to develop a blood pressure waveform (BPW) based deep learning diagnosis model for identifying peripheral arterial disease (PAD) on frequent PAD occurrence arteries. Two issues make it hard to obtain a generalized PAD diagnosis model with a data-driven approach: 1) domain discrepancy resulted from the differences of disease severity and occurring location, 2) data imbalance resulted from the symptomless characteristic of mild PAD. To train a generalized PAD diagnosis model considering practical issues, we propose auxiliary tasks-assisted maximum classifier discrepancy for supervised domain adaptation. The proposed model is validated using virtual patients' BPWs generated from the transmission line model under various disease severity levels. The results show that the proposed model has a superior performance for identifying PAD locations under various disease severity levels. This finding indicates the feasibility of the proposed diagnosis model to real hospitals for identifying the PAD locations in the lower extremities under various disease severity.본 논문의 주요 목적은 말초동맥 질환 빈번 발생 동맥에서 말초 동맥 질환을 식별하기 위한 혈압 파형 기반 딥러닝 진단 모델을 개발하는 것이다. 데이터 기반 방식으로 일반화된 말초동맥 질환 진단 모델을 얻기 위해서는 2가지 문제점이 있다: 1) 질환 심각도와 발병 위치의 차이로 인한 도메인 불일치, 2) 말초동맥 질환 초기 증상이 없다는 특징으로 인한 데이터 불균형. 실제 문제를 고려하여 일반화된 말초동맥 질환 진단 모델 훈련을 위해, 최대 분류 불일치 방법에 두가지 보조 태스크를 추가한 지도 도메인 적응 방법을 제안한다. 제안된 모델은 다양한 질병 심각도 수준에서 전송 선로 모델에서 생성된 가상 환자의 혈압파형을 사용하여 검증된다. 결과는 제안된 모델이 다양한 질병 심각도 수준에서 PAD 위치를 식별하기 위한 우수한 성능을 가지고 있음을 보여준다. 이 결과는 다양한 질병 심각도에서 하지의 PAD 위치를 식별하기 위해 제안된 진단 모델을 실제 병원에 적용할 가능성을 나타낸다.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Structure of the Thesis 3 Chapter 2. Materials and Methods 4 2.1 Problem Definition of naïve data-driven approach 4 2.2 Proposed Method for Training Generalized PAD Diagnosis Model 5 2.2.1 Domain Adaptation 5 2.2.2 Maximum Classifier Discrepancy 6 2.2.3 Proposed Methods 10 2.3 Virtual PAD Patients’ BPW Data Generation 14 2.3.1 Transmission Line Model 14 2.3.2 Setting for Virtual PAD Patients 17 2.3.3 Data Description 18 2.4 Overall Procedure 20 Chapter 3. Results 22 3.1 Compared Methods 22 3.2 Results 22 Chapter 4. Discussion 28 4.1 Efficacy of Proposed Learning Method 28 4.2 Effects of Domain Adaptation 29 4.3 Potential for Practical Applicability 30 Chapter 5. Conclusions 31 5.1 Summary and Contributions 31 5.2 Suggestions for Future Research 32 References 35 Abstract (Korean) 41석

Getting to the Heart of It (Book Review)

Reviews the book, Assessment of Client Core Issues by Richard W. Halstead (see record 2006-21518-000). According to Kays, this book offers a conceptualization of clinical cases synthesizing elements from narrative, schema, and cognitive therapy traditions. The author\u27s stated goal is to present a model that might help counselors think past a symptom-based formulation for diagnosis and incorporate a structured process for analyzing the vast amount of information clients reveal about their struggles in life . Depending heavily on schema therapy concepts originally developed by J. E. Young, Halstead develops an assessment approach that addresses both the unremarkable and problematic client situations. His purpose in writing is to provide therapists with a tool for conceptualizing cases beyond psychiatric symptoms. Halstead demonstrates that focusing on client core issues, in addition to the DSM-IV diagnostic process, provides four advantages: (a) working with presenting clientconcerns, (b) identifying core themes or issues as a treatment focal point, (c) improving systematic monitoring of client progress, and (d) reducing relapse probability by addressing foundational issues. The advantages of this approach make available practical applications for both students and veteran clinicians. Kays notes specific book strengths, as well as a disadvantage

Practical-Oriented Pressure Sensor Placement For Model-Based Leakage Location In Water Distribution Networks

In order to bring remarkable benefits to operation and management of water networks, the analysis of sensed data can be used to locate water leaks using of a model-based methodology. However, the number of sensors installed is usually limited because of budget constraints and hence a strategy for optimizing their number and placement is required. This optimization is tightly coupled to the performance of the real-time model-based leakage diagnosis operation and hence the former should consider the requirements of the latter: (1) high distinguishability among all potential leaks to be detected; and (2) strong robustness in front of model-reality mismatches and other uncertainties. This paper describes a model-based pressure sensor placement optimization technique that focuses on the previous aspects and addresses practicality issues that arise in a real deployment. The technique uses an optimization method based on Genetic Algorithms that, unlike most common approaches in literature, avoids using a binary reasoning process. This increases the information granularity resulting in an improvement of both the leak distinguishability and the method robustness. Moreover, the technique also addresses the practical concerns by deriving an enhanced cost function. Finally, the method is validated in a District Metered Area of the Barcelona water distribution network. Results indicate that a good enough detection accuracy can be achieved with a low number of optimally placed sensors

On the role of Prognostics and Health Management in advanced maintenance systems

The advanced use of the Information and Communication Technologies is evolving the way that systems are managed and maintained. A great number of techniques and methods have emerged in the light of these advances allowing to have an accurate and knowledge about the systems’ condition evolution and remaining useful life. The advances are recognized as outcomes of an innovative discipline, nowadays discussed under the term of Prognostics and Health Management (PHM). In order to analyze how maintenance will change by using PHM, a conceptual model is proposed built upon three views. The model highlights: (i) how PHM may impact the definition of maintenance policies; (ii) how PHM fits within the Condition Based Maintenance (CBM) and (iii) how PHM can be integrated into Reliability Centered Maintenance (RCM) programs. The conceptual model is the research finding of this review note and helps to discuss the role of PHM in advanced maintenance systems.EU Framework Programme Horizon 2020, 645733 - Sustain-Owner - H2020-MSCA-RISE-201