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Medical Metaverse for Digital Healthcare

Technologies, Applications, and Challenges

Edited by R. Nidhya, D. Pavithra, S. Balamurugan, A. Dinesh Kumar and Sheng-Lung Peng
Series: Industry 5.0 Transformation Applications
Copyright: 2026   |   Expected Pub Date:2025/12/30
ISBN: 9781394408474  |  Hardcover  |  
436 pages

One Line Description
Researchers, academics, industry professionals, research and development organizations, and healthcare professionals working in the fields of medical imaging, telemedicine, cybersecurity, data science, artificial intelligence, and image processing.

Audience
Researchers, academics, industry professionals, research and development organizations, and healthcare professionals working in the fields of medical imaging, telemedicine, cybersecurity, data science, artificial intelligence, and image processing.

Description
The rapid evolution of digital technologies is transforming every aspect of human life, and healthcare is no exception. Originally rooted in entertainment and social interaction, the concept of the metaverse is now permeating the medical field, giving rise to what we call the ‘Medical Metaverse’. The metaverse, a shared virtual
space combining physical and digital realities, is poised to offer novel ways to deliver patient care, improve medical training, and enhance patient engagement. This book delves into the exciting and rapidly evolving intersection of healthcare and metaverse technologies. It explores how augmented reality (AR), virtual reality
(VR), artificial intelligence (AI), and other emerging digital tools are revolutionizing medical practices. This book is an essential guide for understanding the technologies that are transforming the healthcare landscape and how they can be applied in practical scenarios.
In addition to providing a comprehensive overview of these technologies, the book dives into real-world applications. From telemedicine and remote surgeries using AI-driven diagnostics and personalized treatments, the book highlights key innovations that are already making an impact. The authors discuss case studies,
practical examples, and the potential for these technologies to address global healthcare challenges, such as improving access to care, reducing costs, and enhancing patient outcomes. Overall, this book provides a holistic understanding of both the promises and the pitfalls of the medical metaverse, making it a valuable resource for healthcare professionals, technologists, policymakers, and researchers alike. Readers will find the volume explores advanced applications of augmented reality (AR), virtual reality (VR), artificial intelligence, and deep and machine learning algorithms for predictive modeling; showcases cuttingedge
innovations emerging from research laboratories with the potential to transform factory-floor operations; demonstrates key risk factors, success factors, and performance metrics that can inform future research and development; and introduces advanced techniques for efficiently addressing the challenges of implementing real-time metaverse applications in healthcare.

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Author / Editor Details
R. Nidhya, PhD is an Assistant Professor in the Department of Computer Science and Engineering, Madanapalle Institute of Technology and Science (Deemed to be University), Andhra Pradesh, India. She has published many research papers in international journals and conferences. Her research interests include wireless body area networks, network security, and data mining.

D. Pavithra, PhD is an Associate Professor at the Dr. NGP Institute of Technology, Coimbatore, Tamil Nadu, India. She completed her PhD at Anna University Chennai in 2021. Her current research interests include autism, machine learning, and deep learning.

S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs and the Vice Chairman of the Renewable Energy Society of India. He also serves as a research consultant to many companies, startups, SMEs, and MSMEs. He has published 57 books, more than 300 international journals and conferences, and 100 patents.

A. Dinesh Kumar is an Associate Professor in the Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation University, Guntur, Andhra Pradesh, India. He completed his PhD at Anna University Chennai in 2018. His current research interests include wireless body area networks, wireless sensor networks, network security, and artificial intelligence.

Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design at the National Taipei University of Business, Taiwan. He has edited several special issues of journals and published more than 100 research papers. His research interests include designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and networks.

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Table of Contents
Preface
Acknowledgement
Part 1: Technologies
1. A Novel Approach for Predicting Human Anomalous Behaviors in Video through Two-Stream 3D Convolutional Neural Network
B. Prabha, J. Nagaraj, Thangarasan T., Soubraylu Sivakumar, S. Velmurugan and Bommy M.
1.1 Introduction
1.2 Related Works
1.3 Materials and Methods
1.3.1 Preprocessing
1.3.2 Data Visualization
1.3.3 Baseline Approach through Support Vector Machine Classifier
1.3.4 Proposed Architecture
1.4 Experimental Results and Discussion
1.4.1 Experimental Setup
1.4.2 Dataset
1.4.3 List of Experiments
1.4.4 Comparative Study
1.5 Conclusion and Future Enhancements
References
2. U-Net–Based Residual Network for Blood Vessel Segmentation from Fundus Images
Nitigya Sambyal, Sukhpal Singh and Varun Gupta
2.1 Introduction
2.2 Related Work
2.3 Proposed Methodology
2.3.1 Dataset
2.3.2 Data Preparation
2.3.2.1 Image Preprocessing
2.3.2.2 Data Augmentation
2.3.3 Proposed U-Net + ResNet34 Model
2.3.4 Model Training
2.3.5 Performance Metrics
2.4 Results and Discussion
2.4.1 Experimental Setup
2.4.2 Hyperparameters
2.4.3 Blood Vessel Segmentation Results
2.4.4 Performance Comparison with other Methods
2.5 Conclusion
References
3. Machine Learning–Based Early Identification of Parkinson Disease Using Voice and Behavioral Data
Smilarubavathy G., R. Nidhya, Keerthana S. M. and Sangeetha S.
3.1 Introduction
3.2 Materials and Methods
3.3 Datasets
3.3.1 Voice Dataset
3.3.2 Tappy Keystroke Dataset
3.3.3 Spiral Drawings Dataset
3.3.4 Gait Analysis Dataset
3.3.5 RBD Patient Dataset
3.4 Results
3.4.1 Voice Data Analysis
3.4.2 Tappy Keystroke Data Analysis
3.4.3 Spiral Drawing Data Analysis
3.4.4 Gait Data Analysis
3.4.5 Voice Analysis in Patients with RBD
3.4.6 Data Analysis Using Logistic ML Model
3.4.7 Analysis of Parkinson Disease versus Healthy Individuals
3.4.8 Analysis of RBD versus PD Patients
3.5 Discussion
3.6 Conclusion
References
4. Automated Dental Age Detection Using Mayfly Optimized Extreme Learning Machine Classification
B. Hemalatha
4.1 Introduction
4.2 Related Works
4.3 Proposed Methodology
4.3.1 System Overview
4.3.2 Dataset Description
4.3.3 Preprocessing
4.3.3.1 Anisotropic Diffusion Filter
4.3.4 Segmentation Using Active Contour Method
4.3.5 Feature Extraction
4.3.6 Feature Classification
4.3.6.1 ELM Classifier
4.3.6.2 MFO Optimization
4.4 Results and Disclosure
4.5 Conclusion
References
5. Ensemble-Stacked Hybrid Classifier (ESHC): A Novel Approach for Imbalanced Data Classification in Stroke Prediction
M. Priyadharshini, Kaushik Sekaran, J. Kalaivani, Ramesh Ponnala and V. Murugesh
5.1 Introduction
5.1.1 Problem Formulation
5.1.2 Objective
5.1.3 Model Architecture
5.2 Related Work
5.2.1 Class Imbalance in the ML
5.2.2 Ensemble Learning for Predictive Modeling
5.2.3 Deep Learning and Neural Networks in Healthcare
5.2.4 Stacking and Hybrid Models
5.2.5 Conditions for the Analysis of Existing Approaches
5.3 Methodology
5.3.1 Dataset and Preprocessing
5.3.2 Handling Class Imbalance with Ensemble Resampling
5.3.3 Feature Extraction Via RF
5.3.4 Stacking Classifiers for Robust Prediction
5.4 Results and Discussion
5.5 Conclusion
5.6 Future Enhancements
References
6. Ensemble-Based Classification and Prediction of Divergent Gene Variant Interpretations Using Boosting and Modern Machine Learning Techniques
SatishKumar Patnala, Umme Najma, D. Chandravathi, Bh. Padma and Jyothi N. M.
6.1 Introduction
6.2 Literature Survey
6.3 Methodology
6.3.1 Ensemble
6.3.2 Booster Models
6.3.3 Bagging and Modern Models
6.3.4 Parameter Setting
6.4 Experimentation
6.5 Results
6.5.1 Cross-Validation with Other Datasets
6.6 Discussion
6.7 Conclusion
References
7. Blockchain-Based AI Security Model for Preserving Medical Data with Interplanetary Access Control System
Rajiv Avacharmal, Brij Kishore Pandey, Piyush Ranjan and Sumit Dahiya
7.1 Introduction
7.2 Related Work
7.3 Proposed Methodology
7.3.1 Blockchain Technology
7.3.1.1 Blockchain Consensus Algorithms
7.3.1.2 Features of Blockchain
7.3.1.3 Classification of Blockchain
7.4 Experimental Results
7.4.1 Experimental Environment
7.4.2 Creation and Realization
7.4.2.1 Network Architecture and Initialization Process
7.4.2.2 Chaincode Installation and Upgrade
7.4.2.3 System Implementation
7.4.3 Results and Discussion
7.5 Conclusion
References
Part 2: Applications
8. Virtual Reality, Augmented Reality, and Mixed Reality for Healthcare Applications
Rajesh Sharma R., Akey Sungheetha, Tina Babu and Rekha R. Nair
8.1 Introduction
8.1.1 Definitions
8.1.2 Relevance to Healthcare
8.1.3 Chapter Objectives
8.2 Literature Review
8.2.1 Medical Education and Training
8.2.2 Surgical Planning and Execution
8.2.3 Rehabilitation and Therapy
8.2.4 Pain Management
8.2.5 Telemedicine and Remote Patient Care
8.3 Methodology
8.3.1 General Framework for Immersive Technology Integration in Healthcare
8.3.2 Data Collection and Processing
8.3.3 Methodologies for Medical Education and Training
8.3.4 Methodologies for Surgical Planning and Execution
8.3.5 Methodologies for Rehabilitation and Therapy
8.3.6 Methodologies for Pain Management
8.3.7 Methodologies for Telemedicine and Remote Patient Care
8.3.8 Evaluation Metrics
8.4 Results and Discussion
8.4.1 Medical Education and Training
8.4.2 Surgical Planning and Execution
8.4.3 Rehabilitation and Therapy
8.4.4 Pain Management
8.4.5 Telemedicine and Remote Patient Care
8.4.6 Overall Discussion
8.5 Conclusion
References
9. A Layered Convolutional Network Model for Cancer Prediction Using Learning Approaches
V. Murugesh, Sanjiv Rao Godla, R.V.V. Krishna, D. Pavithra and R. Nidhya
9.1 Introduction
9.2 Methodology
9.3 Results and Experimentation
9.4 Conclusion
References
10. Chronic Kidney Disease Prediction Using a Layered Convolutional Vector Model
Sanjiv Rao Godla, V. Murugesh, Vijay Kumar Janga, R. Nidhya and D. Pavithra
10.1 Introduction
10.2 Related Works
10.3 Methodology
10.3.1 MRI Data Acquisition
10.3.2 Preprocessing
10.3.3 Image Restoration
10.3.4 Segmentation
10.3.5 Segmenting Function
10.3.6 Classification
10.4 Result Analysis
10.4.1 Experimental Setup
10.4.2 Evaluation
10.4.3 Complexity and Overhead Measure
10.5 Conclusion
References
11. Early Detection and Automatic Diagnosis of Autism Spectrum Disorder Using Transformer Model
S.M. Keerthana, Sasikala, K. Subha and G. Smilarubavathy
11.1 Introduction
11.2 Background Information
11.3 Related Work
11.4 Methodology
11.5 Results
11.6 Discussion and Future Scope
11.7 Conclusion
References
12. Energy-Efficient Lightweight Security Framework for Internet of Medical Things Devices for Healthcare Applications
B. Sarath Chandra, Raja Rao P.B.V., M. Geetha, P. V. Narasimha Raju, Madhukumar Patnala and Sivudu Macherla
12.1 Introduction
12.2 Literature Review
12.3 Methodology
12.4 Results and Discussion
12.5 Conclusion
References
13. An Intelligent Framework for Identifying Tumor in Pancreas through an AI and Deep Learning Approach
Jithendra Reddy Dandu
13.1 Introduction
13.2 Background
13.3 Overview of the Proposed Work
13.3.1 Preprocessing
13.3.2 Segmentation Process
13.3.2.1 Fuzzy C-Means
13.3.2.2 Faster Region-Based Convolutional Neural Network (R-CNN) Segmentation
13.3.3 Image Feature Extraction with GLCM
13.3.4 Classification Process
13.3.4.1 Overview of GNNs
13.4 Results and Discussion
13.4.1 Pancreas Database
13.4.1.1 Input Image
13.4.1.2 Filtering of Pancreas Images
13.4.1.3 Segmentation
13.5 Conclusion
Acknowledgement
References
Part 3: Challenges
14. Performance Improvement of Brain Tumor Classification Using Aquila-Based Optimization on Transfer Learning Models
Ganeshkumar M., Karthigadevi K. and Nesarani A.
14.1 Introduction
14.2 Research Survey
14.3 Model Perspective
14.3.1 Transfer Learning Models
14.3.2 Pretrained Model
14.3.3 Feature Extraction
14.3.4 Need to Tune Hyperparameter of the Learning Model
14.4 Proposed Methodology
14.4.1 Preprocessing Phase
14.4.2 Training and Testing Phase
14.4.3 Transfer Learning Models
14.4.3.1 AlexNet
14.4.3.2 ResNet
14.4.3.3 DenseNet
14.4.3.4 MobileNet
14.4.4 Optimization Algorithm
14.4.4.1 Aquila Optimizer
14.4.4.2 Mathematical Model of AO
14.4.4.3 Arithmetic Optimization Algorithm
14.5 Proposed Methodology
14.6 Results and Discussion
14.6.1 Implementation Test Bed
14.6.2 Evaluation Metrics
14.7 Conclusion
References
15. An Enhanced Pretrained Language Model for Biomedical Domain: Fine-Tuning BERT for Electronic Health Records
Satish Muppidi, Radhika Gouni, Anupama Angadi, Satya Keerthi Gorripati, Venubabu Rachapudi and S. Anjali Devi
15.1 Introduction
15.1.1 Background
15.1.2 Significance
15.2 Approach
15.2.1 Approach 1: MLM
15.2.2 Approach 2: NSP
15.2.3 Fine-Tuning BERT
15.3 Fine-Tuning Experiments
15.3.1 NER
15.3.2 Text Categorization
15.3.3 Association Extraction
15.4 Results
15.4.1 Datasets
15.5 Conclusion
References
16. Development and Optimization of Hybrid Fixed-Wing Unmanned Aerial Vehicles for Healthcare Applications
Pamarthi Venkatasivarambabu and Richa Agrawal
16.1 Introduction
16.2 Literature Review
16.2.1 Review of Design of UAVs
16.2.2 Wing Structure
16.2.3 Path-Planning Techniques
16.3 Methodology
16.3.1 Design of Hybrid Fixed Wing
16.3.2 Optimization of Hybrid Fixed-Wing Design
16.3.3 Mathematical Model Design of Hybrid Fixed-Wing UAV
16.3.4 Path-Planning Techniques Using MCDM
16.4 Results and Discussion
16.5 Conclusion
References
17. Optimization of Nerve Segmentation Performance Using U-Net Architecture and Aquila in Medical Imaging
Umme Najma, B.K. Rajya Lakshmi, D. Chandravathi, Bh. Padma and Jyothi N. M.
17.1 Introduction
17.2 Literature Survey
17.3 Methodology
17.3.1 U-Net
17.3.1.1 Aquila Optimizer
17.3.1.2 The AO Algorithm
17.3.1.3 Combined Optimized Objective Function (U-Net + AO)
17.4 Experimentation
17.4.1 Experimental Steps
17.4.2 Data Preprocessing
17.4.3 Defining Architecture, Parameters, and Hyperparameters
17.4.4 Steps for Training of U-Net Integrated AO
17.5 Results and Metrics
17.6 Discussion
17.7 Conclusion
References
18. AI in Diagnosis, Treatment, and Surgery
Saigurudatta Pamulaparthyvenkata
18.1 Introduction
18.2 Role of AI in Health Diagnosis and Treatment
18.2.1 The Influence of Medical Imaging on Diagnosis
18.2.2 NLP for Efficient Analysis for Information
18.2.3 AI-Powered Decision Assistance Tools
18.2.4 AI-Assisted Robotic Surgery
18.2.5 Virtual Assistants (VA) for Patient Care
18.2.6 Biological Signals: Essential Diagnostics Data
18.3 NLP’S Part in Processing Massive Data for Healthcare Detection
18.3.1 Clinical Text Mining
18.3.2 Diagnosis Support
18.3.3 Early Detection of Diseases
18.3.4 Medical Image Reporting
18.3.5 Patient Risk Stratification
18.3.6 Enhancing Clinical Decision-Making
18.3.7 Identifying Patterns in Patient Data
18.3.8 Predicting Disease Outbreaks
18.4 Applications for AI in Several Surgical Fields
18.4.1 Screening the Population
18.4.2 Identification of Symptomatic Individuals
18.4.3 Preoperative Risk Prediction
18.4.4 Intraoperative Guidance
18.4.5 Operative Robotics
18.5 Results with Meta-Analysis
18.5.1 Recruitment of Studies
18.5.2 Study Characteristics and Validity Assessment
18.5.3 Data Analyses and Synthesis
18.5.3.1 Robotic Surgery Compared with Open Procedure
18.5.3.2 Robotic Surgery Compared with Laparoscopic Procedure
18.6 Discussion
18.7 Ethical Challenges
18.7.1 Patient Confidentiality
18.7.2 Compliance with Regulations
18.7.3 Data Breach Prevention
18.7.4 Anonymization and Deidentification
18.7.5 Access Control and Encryption
18.7.6 Secure Data Sharing and Collaboration
18.7.7 User Training
18.7.8 Financial Barriers
18.8 Conclusion
References
Index

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