Digital Twin for Healthcare: Design, Challenges, and Solutions

Front Cover
Digital Twin for Healthcare
Copyright
Contents
Contributors
1 Introduction
1.1 History of digital twin
1.2 Elements of changes
1.2.1 What has changed regarding content?
1.2.2 Content and the significance of velocity, scope, and impact
1.2.3 Making sense of the data
1.2.4 Touching, smelling and tasting data
1.2.5 Everyone and everything are getting connected
1.2.6 Big brother is watching
1.3 The convergence of technologies
1.4 DT characteristics
1.5 Identify opportunities
References
2 Underactuated digital twin's robotic hands with tactile sensing capabilities for well-being
2.1 Introduction and background
2.2 Humanoid robots
2.3 Additive manufacturing of robotic hands
2.4 Underactuated designs
2.5 Temperature sensors
2.6 Pressure sensors
2.7 Discussion
2.8 Conclusion
References
3 Digital twin for healthcare immersive services: fundamentals, architectures, and open issues
3.1 Introduction
3.2 Fundamentals of DT and XR
3.2.1 Digital twin (DT)
3.2.2 Immersive services
3.2.2.1 Virtual reality (VR)
3.2.2.2 Augmented reality (AR)
3.2.2.3 Mixed reality (MR)
3.2.2.4 Extended reality (XR)
3.2.3 Immersive DT in healthcare: a use case
3.2.3.1 Testing drugs and training professionals
3.2.3.2 Personalized healthcare
3.2.3.3 Telesurgeries
3.3 XR-DT-based system for healthcare requirements
3.3.1 Data collection
3.3.2 Data transmission
3.3.3 Data management
3.3.3.1 DT mechanisms in healthcare
3.3.3.2 Data management in XR for healthcare
Data analysis and 3D construction
Data linking
3.3.4 Visualization and interaction
3.3.4.1 Application graphical interface (GI)
3.3.4.2 Tracking devices
3.4 XR-DT for healthcare architecture: emerging paradigms
3.4.1 Cloud/edge-based hybrid computing architecture
3.4.2 Distributed cooperative data processing: federated learning
3.4.3 Dynamic data storage
3.5 Open issues
3.5.1 Privacy and security
3.5.2 Trust
3.5.3 Dedicated models and approaches
3.5.4 Standardization
3.6 Learned lessons
3.7 Conclusion
References
4 Challenges of Digital Twin in healthcare
4.1 Introduction
4.2 Representation
4.2.1 Types of virtual digital representation
4.2.1.1 Avatars
4.2.1.2 Holograms
4.2.1.3 Robots
4.2.2 Requirements (and challenges)
4.2.2.1 Hyper-fast data rate
4.2.2.2 Extremely low-latency communications (ultra-low delay)
4.2.2.3 Comprehensive end-to-end AI
4.2.2.4 Realistic and accurate trained AI (i.e., avatars)
4.2.2.5 Security
4.2.2.6 Reliability and trust
4.3 Sensing/actuating
4.3.1 Sensing
4.3.1.1 Context
4.3.1.2 Events
4.3.1.3 Data ownership, privacy, and security
4.3.1.4 Reliability
4.3.1.5 Compliance and jurisdiction, legal
4.3.1.6 Interoperability, propriety software and standards
4.3.1.7 Usability and convenience
4.3.1.8 Data misuse
4.3.2 Actuation
4.4 Connectivity
4.4.1 Sensors, sensory networks, and IOT
4.4.2 Connectivity for the AI/ML layer (the intelligence layer)
4.4.3 The representation layer (the intelligence layer)
4.5 Security, privacy, and ethical issues
4.5.1 Security
4.5.2 Privacy and ethical issues
4.5.2.1 Ownership, content, and quality of data
4.5.2.2 Disruption of structures of institutions and roles
4.5.2.3 Inequality and injustice
References
5 Intelligent digital twin reference architecture models for medical and healthcare industry
5.1 Introduction
5.2 Related work
5.3 Challenges
5.4 Digital twins models
5.4.1 Tiers' perspective
5.4.2 Layers' perspective
5.4.2.1 Device layer:
5.4.2.2 Communication layer
5.4.2.3 Service layer
Data sublayer
Function sublayer
5.4.2.4 Application layer
5.4.2.5 Process layer
5.5 DT architecture models
5.5.1 Model 1: single centralized DT management solution instance
5.5.1.1 Discrete DT on single IoT platform
5.5.1.2 Composite DT on single platform
5.5.2 Model 2: distributed DT gateway
5.5.3 Model 3: multiple instance of one DT management solution
5.5.4 Model 4: federated DT gateways
5.5.5 Model 5: multiple DT management solutions
5.5.6 Model summary
5.6 Case study: automatic remote surgeon using robot, DT and VR
5.7 Future direction
References
6 Artificial intelligence models in digital twins for health and well-being
6.1 Background and introduction
6.2 AI in DT models
6.3 Types of AI models in DT for health
6.3.1 Real-time processing
6.3.2 Batch processing
6.3.3 Anomaly
6.3.4 Explainable model
6.3.5 Learning types
6.4 Discussion
6.5 Conclusion
References
7 COVIDMe: a digital twin for COVID-19 self-assessment and detection
7.1 Introduction
7.2 Computer-aided diagnosis
7.3 Digital twin
7.3.1 Digital twin of a person
7.3.2 Digital twin for health
7.4 COVIDMe and the spread of COVID-19
7.4.1 Automatic detection of COVID-19
7.5 An overview of the COVIDMe software architecture
7.5.1 Use-case diagram
Start assessment
Preprocess data
Screen for COVID-19
Store screening results
Present RT with QOE-based feedback
Update health recommendations
7.5.2 Communication diagram
7.6 Discussion and future work
7.7 Conclusions
References
8 Improving human living environment and human health through environmental digital twins technology
8.1 Introduction
8.2 Parameter identification and uncertainty estimation of the DTs model for central air-conditioning
8.2.1 Construction of the DTs sewage treatment platform
8.2.2 Parameter identification of the equipment model of central air-conditioning water system based on genetic algorithm (GA)
8.2.3 Prediction interval estimation of the central air-conditioning model based on the K-means clustering algorithm
8.2.4 Error compensation for the equipment model of central air-conditioning water system based on ANN
8.2.5 Case analysis of algorithm performance
8.3 Results and discussion
8.3.1 Results of parameter identification based on GA and MISSO
8.3.2 Results of prediction interval estimation of central air-conditioning model based on K-means clustering algorithm
8.3.3 Residual error compensation results of the model based on ANN
8.4 Conclusion
References
9 Role of smart technologies in detecting cognitive impairment and enhancing assisted living
9.1 Introduction
9.2 Mild cognitive impairment (MCI) detection
9.2.1 Using gait patterns and postural dynamics
9.2.2 Using physiological changes in ECG and EEG
9.2.3 By tracking eye movement
9.2.4 Sleep monitoring
9.2.5 Using handwriting
9.2.6 Using multiple signals (smart homes)
9.3 Providing assisted living
9.3.1 By using augmented reality (AR)
9.3.2 By managing wandering
9.3.3 By analyzing emotional fluctuations
9.4 Conclusion
Acknowledgments
References
10 Digital twins and cybersecurity in healthcare systems
10.1 Introduction
10.2 Digital twin opportunities in cyber security
10.2.1 Improving security design and testing
10.2.2 Support better intrusion detection
10.2.3 Enhance privacy controls
10.3 Digital twin cyber security framework
10.3.1 Digital twins threat modeling in health care
10.3.2 Common attacks on digital twins medical devices
10.3.3 Digital twin authentication and identification challenge
10.3.4 Building cyber resilience in digital twins
10.3.4.1 Stronger IDS
10.3.4.2 Stronger intrusion prevention system (IPS)
10.3.4.3 Future digital twin authentication methods
Channel characteristics variation authentication
Radio frequency (RF) fingerprinting
Biometric authentication
10.3.4.4 Protecting the communication channel for digital twins
10.4 Digital twin privacy framework
10.4.1 Lack of privacy and trust challenge
10.4.2 Privacy by design
10.4.3 Enhancing trust with block chain integration
10.5 Digital twins compliance with standards and governance
10.6 Conclusion
References
11 Potential applications of digital twin in medical care
11.1 Foundations for potential applications of digital twins in medical care
11.1.1 Digital health criteria
11.1.2 Digital health regulatory policies
11.1.3 Digital health center for excellence
11.1.4 Network of digital health experts
11.2 Applications of digital twin in medical care: state of the art
11.2.1 Personal health management
11.2.1.1 Personal health and well-being
11.2.1.2 Personal health
11.2.2 Precision medicine
11.2.2.1 Personalized medicine
Cardiovascular medicine
11.2.2.2 Drug management
11.2.2.3 Diseases and treatment
11.3 Future applications of digital twin in medical care
11.3.1 Monitoring
11.3.2 Diagnosis
11.3.3 Surgery planning: simulation and risk assessment
11.3.4 Medical devices
11.3.5 Drug development
References
12 Digital twins for decision support system for clinicians and hospital to reduce error rate
12.1 Introduction to digital twin decision support system for reducing errors in hospitals
12.2 Why we need the digital twin system to reduce errors in hospitals
12.3 What is digital twin for decision support system to reduce errors
12.3.1 Conceptual diagram
12.3.1.1 Key components of the DSS are as follows
1. Patient centric digital twin data set
2. Aggregated digital twin data set at hospitals systems
3. Questionnaire dataset
4. Recommendations dataset
12.3.2 Digital twin for decision support system (DSS)
12.3.3 Key components, definitions, challenges, and data sources
12.3.3.1 Patient health record (PHR)
12.3.3.2 Electronic health records (EHR)
12.3.3.3 Electronic medical records (EMR)
12.3.4 Type of data available and key consideration while building the DSS
12.3.4.1 Possible data sources for decision support system to reduce errors
12.4 Digital twin platform for decision support system to reduce errors
12.4.1 Infrastructure layer
12.4.2 Data layer
12.4.3 Application layer
12.4.4 Security and trust layer
12.4.5 Management and orchestration layer
12.5 Digital twin system deployment, evaluation and operational consideration
12.5.1 Output action pairing (OAP)
12.5.2 DSS deployment considerations
12.6 Digital twin for decision support system challenges
12.7 Example case studies – DSS
12.8 Conclusion
References
13 Digital twin for cardiology
13.1 Introduction to digital twin for cardiology
13.1.1 History
13.1.2 Focus
13.1.3 Facts
13.2 Digital twins to challenge heart disease
13.2.1 Opportunities
13.2.2 Digital twin structures for cardiology
13.2.3 Bring your own data (BYOD)
13.2.4 Timely data sharing
13.2.5 Opportunities
13.3 Digital twin for cardiology futures
13.3.1 New software by doctors for doctors
13.3.2 Personalization of evidence based medicine
13.4 Conclusion
Acknowledgments
References
14 Applications of Digital Twins to migraine
14.1 Introduction
14.2 Migraine disease
14.2.1 Definitions and complexities related to treatment processes
14.2.2 Classification, symptoms, and diagnosis process
14.2.3 Attack triggers and their complexity
14.2.4 Treatment processes in migraine
14.3 Digital Twins technology: definitions, required technologies and applications
14.3.1 Required technologies
14.3.2 Applications of Digital Twin
14.4 Applications of Digital Twins Technology to migraine disease
14.4.1 Challenges of migraine disease and the importance of personalized medicine
14.5 Digital Twin solutions for migraine disease
14.5.1 Applicability of cutting-edge technologies for migraine disease
14.5.2 Problem of existing solutions
14.5.3 Possible solutions of Digital Twins technology for migraine disease
14.6 Discussion
14.7 Conclusion
Acknowledgment
References
15 Digital twins for nutrition
15.1 Introduction
15.1.1 Nutrition concepts
15.1.2 Advanced technology in nutrition
15.1.3 Personalized nutrition of food
15.1.4 Digital twins in nutrition
15.1.5 Contribution of the paper
15.2 Related work
15.3 Research methodology
15.4 Documentation on DT and nutrition
15.5 Ecosystem of the digital twin for nutrition
15.5.1 Data source
15.5.2 AI interface
15.5.3 Multimodal interaction (MMI)
15.6 Case study: hair loss
15.7 Discussion
15.8 Conclusion
Clearly the lessons learned
Acknowledgment
References
16 Digital twins for allergies
16.1 Introduction
16.2 Related works
16.2.1 Internet of things (IoT)
16.2.2 Machine learning (ML)
16.2.3 Blockchain technology
16.2.4 Cloud and fog computing
16.2.5 5G and 6G wireless communication
16.2.6 AR/VR/Mix reality
16.2.7 Simulation techniques
16.3 Ecosystem of the DT for allergy disease
16.3.1 Allergy data source
16.3.2 AI interface
16.3.3 Multimodal interaction
16.4 Case study: anaphylaxis shocks
16.5 Discussion
16.6 Conclusion
Clearly the lessons learned
Acknowledgment
References
Index
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