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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