This book comprehensively reviews the potential of Artificial Intelligence (AI) in biomedical research and healthcare, with a major emphasis on virology. The initial chapter presents the applications of machine learning methods for structured data, such as the classical support vector machine and neural network, modern deep learning, and natural language processing for unstructured data in biomedical research and healthcare. The subsequent chapters explore the applications of AI in tackling COVID-19, analysis of the pandemic, viral infection, disease spread, and control. The book further identifies the potential applications of machine learning in the field of virology with a focus on the key aspects of infection: diagnosis, transmission, response to treatment, and resistance. The book also discusses progress and challenges in developing viral vaccines and examines the application of viruses in translational research and human healthcare.
Furthermore, the book covers the applications of artificial intelligence-mediated diagnosis and the development of drugs to treat the disease. Towards the end, the book summarizes the ethical and legal challenges posed by AI in healthcare and biomedical research. This book is an invaluable source for researchers, medical and industry practitioners, academicians, and students exploring the applications of AI in biomedical research and healthcare.
Author(s): Jyotir Moy Chatterjee; Shailendra K. Saxena
Publisher: Springer Nature Singapore
Year: 2023
Language: English
Pages: 202
Foreword
Preface
Acknowledgements
Contents
Editors and Contributors
1: Artificial Intelligence for Global Healthcare
1.1 Introduction
1.2 Role of Artificial Intelligence (AI) in Public Health
1.2.1 Health Protection
1.2.1.1 Disease Detection
1.2.1.2 Data Pattern Analysis for Near-Real-Time Surveillance
1.2.2 Health Promotion
1.2.3 Improving the Efficiency of Healthcare Services
1.2.3.1 Detecting Diabetic Retinopathy
1.2.3.2 Health Informatics and Electronic Medical Records
1.2.3.3 Booking Appointments
1.2.3.4 Surgical Assistance
1.3 Global Health Challenges
1.3.1 Impact of Data Biases
1.3.2 Absence or Insufficiency of IT Infrastructure
1.3.3 Trustworthiness of AI
1.3.4 Importance and Impact of AI Laws
1.3.5 Societal Acceptance
1.4 Artificial Intelligence and Its Opportunities in Global Health
1.4.1 AI-Interventions and Application Areas
1.4.2 Artificial Intelligence Opportunities in Global Health
1.5 Demand and Drawbacks of AI in the Context of Global Health
1.6 Conclusion
References
2: Artificial Intelligence for Epidemiology COVID-19: Quick Assessment
2.1 Introduction
2.1.1 In the Future We Will Link Our Brains with Artificial Intelligence Systems
2.1.2 Terrible COVID-19
2.2 Related Works
2.2.1 Limitations in the Literature Study Are As Follows
2.3 Artificial Intelligence Methods and Applications for Healthcare
2.3.1 Artificial Intelligence Methods for Healthcare
2.3.2 Artificial Intelligence Applications for HealthCare
2.3.3 Some More AI Applications for Healthcare
2.4 Summary
References
3: Artificial Intelligence in Rural Health in Developing Countries
3.1 Healthcare Delivery in Rural Areas
3.2 AI in Resource-Limited Settings
3.3 Six Principles of AI by the World Health Organization
3.4 Challenges in Development and Implementation of AI
3.5 Telemedicine and Data Collection
3.6 Processing of Data
3.7 Devices and Instruments that May Help in Developing AI in Rural Area
3.8 Example of AI Application in a Rural Areas
3.9 Summary
References
4: The Role of Artificial Intelligence to Track COVID-19 Disease
4.1 Introduction
4.2 Literature Review
4.3 Diagnosis and Tracking
4.4 Treatment and Vaccination
4.5 Precautions and Social Control
4.6 Conclusion
References
5: Artificial Intelligence Techniques Based on K-MeansTwo Way Clustering and Greedy Triclustering Approach for 3D Gene Express...
5.1 Introduction
5.2 Background Study
5.2.1 Issues in the Literature
5.3 Proposed Work
5.3.1 Mean Correlation Value Equation for Tricluster (TriMCV)
5.3.2 Function for Fitness
5.3.3 Description of K-MeansTwo Way
5.3.3.1 Tricluster Generation Using K-MeansTwo Way Clustering
Algorithm 5.1 Tricluster Seed Formation Step Using K-MeansTwo Way Clustering
5.3.4 Specification of Greedy Triclustering
Algorithm 5.2 Greedy Triclustering (GreedyTri)
5.4 Result and Analysis
5.4.1 CDC15 Experiment Using 3D GED
5.4.2 Elutriation Experiment Using 3D GED
5.4.3 Pheromone Experiment Using 3D GED
5.5 Summary
References
6: Detection of COVID-19 Cases from X-Ray and CT Images Using Transfer Learning and Deep Convolution Neural Networks
6.1 Introduction
6.2 Related Works
6.3 Experiment Setup
6.3.1 Dataset Description
6.3.2 DCNN
6.3.2.1 Inception V3
6.3.2.2 VGG-16
6.3.2.3 VGG-19
6.3.3 Parameters Information
6.3.3.1 Adam
6.3.3.2 Stochastic Gradient Descent (SGD)
6.3.3.3 Limited Memory: Broyden-Fletcher-Goldfarb-Shanno Algorithm (L-BFGS-B)
6.3.3.4 tanh
6.3.3.5 Identity
6.3.3.6 Logistic
6.3.3.7 ReLu
6.3.4 Evaluation and Classification
6.4 Conclusion and Future Work
References
7: Computer Vision: A Detailed Review onAugmented Reality (AR), Virtual Reality (VR), Telehealth, and Digital Radiology
7.1 Introduction
7.2 Literature Review
7.3 Applications of Computer Vision
7.3.1 Computer Vision in Sports
7.3.1.1 Sports Production
7.3.1.2 Player Tracking
7.3.1.3 Ball Tracking
7.3.2 Computer Vision in Health and Medicine
7.3.2.1 Cancer Detection
7.3.2.2 Cell Classification
7.3.2.3 Tumor Detection
7.3.2.4 Development Analysis
7.3.2.5 Cover Detection
7.3.3 Computer Vision in Agriculture and Farming
7.3.3.1 Absconds in Agriculture
7.3.3.2 Counting
7.3.3.3 Plant Recognition (Fig. 7.3)
7.3.3.4 Animal Monitoring
7.3.3.5 Farm Automation
7.3.4 Computer Vision in Retail and Manufacturing (Fig. 7.4)
7.3.4.1 Customer Tracking
7.3.4.2 Individuals Counting
7.3.4.3 Thief Detection
7.3.4.4 Waiting Time Analytic
7.3.4.5 Social Distance
7.3.4.6 Productivity Analytics
7.3.4.7 Quality Management
7.4 Advantages
7.4.1 Impact of Computer Vision
7.4.1.1 Financial Services (Fig. 7.5)
7.4.2 Insurance
7.4.3 Capital Markets
7.4.4 Commerce
7.4.5 Banking
7.5 Future of Computer Vision
7.6 AR, VR and What CV Means to Them
7.7 Computer Vision and Augmented Reality for E-Commerce
7.8 Conclusion
References
8: Stroke Disease Prediction Model Using ANOVA with Classification Algorithms
8.1 Background of the Study
8.2 Related Works
8.3 Materials and Methods
8.3.1 Classification
8.4 Results and Discussions
8.5 Conclusion
Appendix
References
9: A Concise Review on Developmental and Evaluation Methods of Artificial Intelligence on COVID-19 Detection
9.1 Introduction
9.2 Diagnosis of COVID-19 with Machine Learning and Deep Learning Approaches
9.3 Review on Benchmark Dataset Utilized for the Assessment of Prevailing Artificial Intelligence Approaches
9.4 Comparative Analysis of Various Nature Inspired and Other Deep Leaning Algorithms on the Detection of COVID-19 Detection
9.5 Dealing with COVID-19 Application of Artificial Intelligence and Machine Learning
9.5.1 Early Detection and Prompt Diagnosis
9.5.2 Treatment Monitoring
9.5.3 Contact Tracing
9.5.4 Mortality Rate
9.5.5 Vaccine and Drug Development
9.5.6 Work Load Reduction of Healthcare Experts
9.5.7 Disease Prevention
9.6 Shortcomings of Current Methods
9.7 Critical Analysis
9.8 Conclusion
References
10: Artificial Intelligence-Based Healthcare Industry 4.0 for Disease Detection Using Machine Learning Techniques
10.1 Introduction
10.2 Framework to Apply Machine Learning for Disease Detection
10.3 Current Trends in Disease Detection: Machine Learning perspective
10.4 The Case Studies
10.4.1 Predicting the Heart Disease: Case Study I
10.4.2 COVID-19 Detection-Case Study II
10.5 Conclusion and Future Scope
References
11: Deep Autoencoder Neural Networks for Heart Sound Classification
11.1 Introduction
11.2 Literature Survey
11.3 Proposed Algorithm
11.3.1 PCG Signal Acquisition
11.3.2 Pre-processing Block
11.3.3 Time-Frequency Image Representation
11.3.4 Stacked Autoencoder (SAEN) Neural Network and Softmax Classifier
Algorithm 11.1 The proposed algorithm of heart sound classification method
11.4 Pre-processing and Time-Frequency Image Generation
11.4.1 Infinite Impulse Response-Constant-Q Transform (IIR-CQT)
11.5 Deep Autoencoder Neural Network-Based Classification
11.6 Experimental Results and Discussions
11.6.1 IoT-Based Application of the Proposed Method
11.7 Conclusion
References
