دانلود کتاب Handbook of Artificial Intelligence in Healthcare: Vol. 1 - Advances and Applications

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Preface
Contents
Part I Advances in AI for Healthcare Signal, Image, and Video Processing
1 Advances in Artificial Intelligence for the Identification of Epileptiform Discharges
1.1 Background
1.2 Artificial Intelligence Tools
1.3 Pre-Ictal, Ictal, Post-Ictal Detection
1.4 Seizure Detection
1.5 Inter-Ictal Identification
1.6 Seizure Onset Zone
1.7 Implications and Future Challenges
References
2 Characterizing EEG Electrodes in Directed Functional Brain Networks Using Normalized Transfer Entropy and PageRank
2.1 Introduction
2.2 Current Approaches to Study Directional Information Flow in FBNs
2.2.1 Normalized Transfer Entropy
2.2.2 PageRank
2.3 Materials and Methods
2.3.1 Experimental Design
2.3.2 EEG Data Acquisition and Pre-Processing
2.3.3 Behavioral Data
2.3.4 Directed Information Flow Using Normalized Transfer Entropy
2.3.5 Rate of Change of Cognition
2.4 Experimental Results and Discussion
2.4.1 Electrode Wise Analysis
2.4.2 Observation Phase
2.4.3 Entire Population Group-Wise Analysis
2.5 Conclusion
References
3 Autistic Verbal Behavior Language Parameterization
3.1 Introduction
3.2 Considerations About the Autistic Spectrum Disorder
3.2.1 Degrees of Autism
3.2.2 Verbal Behavior
3.3 Materials and Methods
3.3.1 Hardware
3.3.2 Protocol
3.3.3 Software
3.4 Preliminary Evaluation
3.4.1 Modeling the Problem with Metadata
3.4.2 Advantages and Disadvantages of Using the Proposed Approach
3.5 The Sounds of the Use-Case
3.6 Test and Evaluation
3.6.1 Pre-processing
3.6.2 Variable Selection
3.6.3 Automatic Timestamp Detection
3.6.4 Model for Timestamp Detection
3.6.5 Model Findings and Results Analysis
3.7 Conclusions and Future Work
References
4 Case Studies to Demonstrate Real-World Applications in Ophthalmic Image Analysis
4.1 Introduction
4.2 Related Work
4.2.1 Retinal Image Quality Assessment
4.2.2 Arteriolar-to-Venular Index and A/V Classification
4.2.3 Retinopathy of Prematurity
4.3 Case Study: Retinal Quality Assessment
4.3.1 Dataset
4.3.2 Methods
4.3.3 Results
4.4 Case Study: Arteriolar-to-Venular Index
4.4.1 Datasets
4.4.2 Methods
4.4.3 Results
4.5 Case Study: Retinopathy of Prematurity
4.5.1 Datasets
4.5.2 Methods
4.5.3 Results
4.6 Summary
References
5 Segmentation of Petri Plate Images for Automatic Reporting of Urine Culture Tests
5.1 Introduction
5.2 Related Work
5.3 Automatic Petri Plate Analysis Pipeline
5.3.1 Image Acquisition
5.3.2 Segmentation
5.3.3 Colony Classification and Count
5.4 Conclusions
References
6 Repurposing Routine Imaging for Cancer Biomarker Discovery Using Machine Learning
6.1 Introduction
6.1.1 Imaging Modalities in Cancer Care
6.1.2 Imaging in the Cancer Pathway
6.2 Cancer Biomarker Research
6.3 Machine Learning Applications in Cancer Cross-Sectional Imaging
6.3.1 Lesion Detection/Classification
6.3.2 Segmentation
6.3.3 Cancer-Related Radiomics
6.4 Preparing Radiology Data for Machine Learning
6.5 Example of Biomarker Discovery: Sarcopenia in Cancer
6.5.1 Defining Cancer Sarcopenia
6.5.2 Scalable Solutions to Radiological Sarcopenia Assessment
6.5.3 Remaining Translational Gaps
6.6 Conclusion
References
7 Automatic Detection of LST-Type Polyp by CNN Using Depth Map
7.1 Introduction
7.2 Background
7.2.1 Removal of Specular Reflectance Components and Generation of Lambertian Images
7.2.2 Recovering 3D Shape and Creating Depth Map
7.3 Construction of U-Net Using Depth Map
7.3.1 Preprocessing and Construction of Dataset
7.3.2 Construction of CNN Model Using U-Net Structure
7.4 Experiment
7.4.1 Evaluation Method
7.4.2 Detection Experiment
7.5 Conclusion
References
8 Artificial Intelligence and Deep Learning, Important Tools in Assisting Gastroenterologists
8.1 Introduction
8.2 Computer-Assisted Colonoscopy for CRC Early Detection
8.2.1 Polyps’ Semantic Segmentation
8.2.2 Reviews and Meta-Analysis, Randomized Studies and AI Embedded Colonoscopy Devices
8.2.3 Well Structured Labeled Databases
8.3 Dealing with Video Colonoscopy Frames
8.4 Deep Learning on Video Colonoscopies
8.4.1 Deep Learning on Video Colonoscopies Using Nvidia Jetson Xavier
8.5 Conclusions
References
9 Last Advances on Automatic Carotid Artery Analysis in Ultrasound Images: Towards Deep Learning
9.1 Introduction
9.2 Carotid Artery Segmentation and Intima Media Thickness Estimation in Ultrasound Images
9.2.1 Deep Learning Proposal for IMT Estimation and Plaque Detection
9.3 Carotid Artery Plaque Classification and Risk Assessment in 2D CA Ultrasound Images
9.3.1 Data Properties: Transversal/Follow-Up, Different Devices, Image Modality, Artery Territory, Number of Samples and Ground Truth
9.3.2 Work Objectives
9.3.3 Image Features
9.3.4 Methods and Results
9.4 Discussion: Challenges in Deep Learning
9.5 Conclusions and Future Perspective
9.6 Appendix
References
10 Radiomics and Its Application in Predicting Microvascular Invasion of Hepatocellular Carcinoma
10.1 Introduction
10.1.1 What is Radiomics
10.1.2 What Has Been Achieved in Medical Image Analysis Using Radiomics
10.1.3 Application of Radiomics in Hepatocellular Carcinoma
10.2 Radiomics Signature and Prediction Model
10.2.1 Medical Image Acquisition
10.2.2 Calibration and Segmentation of Tumour Regions
10.2.3 Feature Extraction and Quantification
10.2.4 Feature Selection
10.2.5 Classification and Prediction
10.2.6 Material and Clinical Model
10.2.7 Radiomics Model and Fusion Model for Predicting MVI
10.3 Experiment
10.3.1 Experimental Result
10.3.2 The Direction of Future Progress
10.4 Conclusion
References
11 Artificial Intelligence in Remote Photoplethysmography: Remote Heart Rate Estimation from Video Images
11.1 Introduction
11.2 Naive Methods
11.3 Blind Signal Separation
11.3.1 Independent Component Analysis
11.3.2 Principal Component Analysis
11.3.3 Joint Blind Signal Separation
11.4 Modelling
11.4.1 CHROM
11.4.2 Illumination Rectification
11.4.3 2SR, POS
11.4.4 Motion Reduction
11.5 Deep Learning
11.5.1 Feature Extraction and Representation
11.5.2 Interference Separation and Signal Enhancement
11.6 Popular Datasets for rPPG Learning
11.7 Future
References
Part II Advances in AI for Healthcare Information and Data Analytics
12 Mining Data to Deal with Epidemics: Case Studies to Demonstrate Real World AI Applications
12.1 Introduction
12.1.1 Goal and Research Questions
12.1.2 Introduction to Data Mining
12.1.3 Data Mining Techniques
12.1.4 Chapter Overview
12.2 Literature Review
12.2.1 Dengue Fever Analysis and Prediction with Classification and Association Rules
12.2.2 Mumps Analysis with Clustering and Association Rules
12.2.3 Cholera Analysis with Classification and Association Rules
12.2.4 Measles Analysis with Classification
12.2.5 Ebola Analysis with Clustering
12.3 Methodology
12.3.1 Methodology Outline
12.4 Experiments
12.4.1 Dataset
12.4.2 Classification
12.4.3 Clustering
12.4.4 Association Rule Mining
12.5 Conclusion
12.5.1 Discussion
12.5.2 Overview of Contribution
12.5.3 Future Directions
References
13 A Powerful Holonic and Multi-Agent-Based Front-End for Medical Diagnostics Systems
13.1 Introduction
13.2 State of the Art
13.3 Differential Diagnosis and the Holonic Medical Diagnostics System (HMDS)
13.3.1 Differential Diagnosis as a Holonic Domain
13.3.2 The Holonic Medical Diagnostics System
13.4 Learning in the HMDS
13.5 Simulations
13.5.1 The Assessment of the Diagnosis Abilities
13.5.2 The Assessment of the Self-Organization Abilities
13.6 Discussion
13.7 Conclusion
References
14 Computer-Aided Detection of Depressive Severity Using Multimodal Behavioral Data
14.1 Introduction
14.2 Multimodal Behavioral Dataset of Chinese University Students with and Without Depressive Tendencies
14.2.1 Collecting Survey Data
14.2.2 Acquiring Behavioral Data
14.3 Computer-Aided Detection of Depressive Severity
14.3.1 Feature Extraction
14.3.2 Detection Model
14.4 Performance Evaluation
14.4.1 Experimental Setup
14.4.2 Evaluation Functions
14.4.3 Results
14.5 Conclusions
References
15 Classifying Process Traces for Stroke Management Quality Assessment: A Deep Learning Approach
15.1 Introduction
15.2 Background
15.2.1 Convolutional Neural Networks
15.2.2 Autoencoders
15.2.3 Recurrent Neural Networks
15.3 Related Work
15.4 Deep Learning Process Trace Classification for Quality Assessment
15.5 Experimental Results
15.6 Discussion and Conclusions
References
16 Synergy-Net: Artificial Intelligence at the Service of Oncological Prevention
16.1 Introduction
16.2 Synergy-Net
16.2.1 Medical Imaging and AI
16.2.2 The Synergy-Net Architecture
16.2.3 Synergy-Net: Analysed Tumours
16.3 Skin Cancer
16.4 Lung
16.5 Colon Rectum Cancer
16.6 Breast Cancer
16.7 Gastric Carcinoma
16.8 Thyroid Cancer
16.9 Prostate Cancer
16.10 Conclusions and Future Perspectives
References
17 New Insights on Implementing and Evaluating Artificial Intelligence in Cardiovascular Care
17.1 Introduction
17.1.1 Artificial Intelligence and Machine Learning
17.1.2 Relevance of Artificial Intelligence to the Future of Cardiovascular Care Delivery
17.1.3 Implementing AI Within Institutional Healthcare Environments
17.2 Data Capture and Management
17.2.1 Data Availability
17.2.2 Data Quality
17.2.3 Data Generalizability
17.2.4 Missing Data
17.2.5 Data Permission and Privacy
17.3 Model Development and Validation
17.3.1 Model Development
17.3.2 Model Performance Metrics May not Reflect Clinical Applicability
17.3.3 Model Generalizability and Explainability
17.3.4 Algorithmic Bias and Equity, Diversity and Inclusion
17.4 Clinical Integration and Support
17.4.1 Human Barriers
17.4.2 Regulatory Considerations and Demonstrating Patient Value
17.5 Chapter Summary
References