Digital Twin Technologies for Healthcare 4.0

Cover
Contents
About the editors
1 Introduction: digital twin technology in healthcare
	1.1 Introduction
	1.2 Digital twin – background study
	1.3 Research on digital twin technologies
	1.4 Digital twin sectors in healthcare
		1.4.1 Digital patient
		1.4.2 Pharmaceutical industry
		1.4.3 Hospital
		1.4.4 Wearable technologies
	1.5 Challenges and issues in implementation
		1.5.1 Trust
		1.5.2 Security and privacy
		1.5.3 Standardization
		1.5.4 Diversity and multisource
	References
2 Convergence of Digital Twin, AI, IOT, and machine learning techniques for medical diagnostics
	2.1 Introduction
	2.2 DT technology
		2.2.1 Steps in DT creation
		2.2.2 DT types and functions
	2.3 DT and its supporting technologies – AI, Cloud computing, DL, Big Data analytics, ML, and IoT
	2.4 DT integration with other technologies for medical diagnosis and health management
	2.5 DT technology and its application
		2.5.1 DT application in manufacturing industry
		2.5.2 Applications of DT in automotive & aerospace
		2.5.3 Medicine diagnosis and device development
		2.5.4 Wind twin technology
	2.6 Conclusion
	References
3 Application of digital twin technology in model-based systems engineering
	3.1 Evolution of DTT
	3.2 Basic concepts of DTT
	3.3 DTT implementation in power system
		3.3.1 Characteristics of DTT in power systems
	3.4 Power system network modeling using DTT
		3.4.1 Model-based approach
		3.4.2 Data-driven approach
		3.4.3 Combination of both
	3.5 Integration of power system with DTT
	3.6 Future scope of DTT in power systems
	3.7 Conclusion
	References
4 Digital twins in e-health: adoption of technology and challenges in the management of clinical systems
	4.1 Introduction
	4.2 Digital twin
	4.3 Evolution of healthcare services
	4.4 Elderly medical services and demands
	4.5 Cloud computing
	4.6 Cloud computing DT in healthcare
		4.6.1 Use cases
	4.7 Digital healthcare modeling process
	4.8 Cloud-based healthcare facility platform
	4.9 Applications of DT technology
		4.9.1 Cardiovascular application
		4.9.2 Cadaver high temperature
		4.9.3 Diabetes meters
		4.9.4 Stress monitoring
	4.10 Benefits of DT technology
		4.10.1 Remote monitoring
		4.10.2 Group cooperation
		4.10.3 Analytical maintenance
		4.10.4 Transparency
		4.10.5 Future prediction
		4.10.6 Information
		4.10.7 Big data analytics and processing
		4.10.8 Cost effectiveness
	4.11 DT challenges in healthcare
		4.11.1 Cost effectiveness
		4.11.2 Data collection
		4.11.3 Data protection
		4.11.4 Team collaboration
		4.11.5 Monitoring
		4.11.6 Software maintenance and assurance
		4.11.7 Regulatory complications
		4.11.8 Security and privacy-related issues
		4.11.9 Targets of attackers
	4.12 Conclusion
	References
5 Digital twin and big data in healthcare systems
	5.1 Introduction
		5.1.1 Working of DT technology
	5.2 Need for DT and big data in healthcare
	5.3 DT and big data benefits for healthcare
		5.3.1 Monitoring of patients
		5.3.2 Individualized medical care
		5.3.3 Patient individuality and freedom
	5.4 Applications of DT in healthcare
		5.4.1 Diagnosis and decision support
		5.4.2 Patient monitoring
		5.4.3 Drug and medical device development
		5.4.4 Personalized medicine
		5.4.5 Medical imaging and wearables
	5.5 Enabling technologies for DT and data analytics in healthcare
		5.5.1 Technologies for DT in healthcare
		5.5.2 Technologies for data analytics in healthcare
	5.6 Research challenges of DT and big data in healthcare
		5.6.1 Problem complexities and challenges
		5.6.2 Research challenges for DT in healthcare
		5.6.3 Useful information
	5.7 Future research directions
	5.8 Conclusion
	References
6 Digital twin data visualization techniques
	6.1 Introduction – twin digital
	6.2 Invention of DT
		6.2.1 Function of DT technology
		6.2.2 What problems has it solved?
	6.3 DT types
		6.3.1 Parts twinning
		6.3.2 Product twinning
		6.3.3 System twinning
		6.3.4 Process twinning
	6.4 When to use
	6.5 Design DT
		6.5.1 Digital data
		6.5.2 Models
		6.5.3 Linking
		6.5.4 Examples
		6.5.5 How has it impacted the industry?
		6.5.6 DT usage
	6.6 DT technology’s characteristics
		6.6.1 Connectivity
		6.6.2 Homogenization
		6.6.3 Reprogrammable
		6.6.4 Digital traces
		6.6.5 Modularity
	6.7 Twin data to data
		6.7.1 Requirements for obtaining complete data
		6.7.2 Requirements on knowledge mining
		6.7.3 Data fusion in real time
		6.7.4 Data interaction in real time
		6.7.5 Optimization in phases
		6.7.6 On-demand data usage
		6.7.7 Data composed of DTs
	6.8 Data principles for DTs
		6.8.1 Principle of complementary
		6.8.2 The principle of standardization
		6.8.3 The principle of timeliness
		6.8.4 The association principle
		6.8.5 Fusion principle
		6.8.6 Information growth principle
		6.8.7 The principle of servitization
	6.9 DTD methodology
		6.9.1 Information gathering for the DT
		6.9.2 Data storage of DTs
		6.9.3 DT data interaction
		6.9.4 Association of DT data
		6.9.5 Fusion of data from DTs
		6.9.6 Data evolution in the DT
		6.9.7 Data servitization for the DT
		6.9.8 DT data’s key enabler technologies
		6.9.9 Advantages of DT
		6.9.10 Disadvantages of DT
	6.10 Conclusion
	References
7 Healthcare cyberspace: medical cyber physical system in digital twin
	7.1 Introduction
	7.2 Cyber physical systems
	7.3 Digital twin
	7.4 DT in healthcare
		7.4.1 Patient monitoring using DT
		7.4.2 Operational efficiency in hospital using DT
		7.4.3 Medical equipment and DT
		7.4.4 DT in device development
	7.5 Applications of DT in healthcare
		7.5.1 Patient monitoring using DT
		7.5.2 Medical wearables
		7.5.3 Medical tests and procedures
		7.5.4 Medical device optimization
		7.5.5 Drug development
		7.5.6 Regulatory services
	7.6 DT framework in healthcare
		7.6.1 Prediction phase
		7.6.2 Monitoring phase
		7.6.3 Comparison phase
	7.7 Cyber resilience in healthcare DT
	7.8 Cyber physical system and DT
		7.8.1 Mapping in CPS and DTs
		7.8.2 Unit level
		7.8.3 System level
		7.8.4 SoS level
	7.9 Advantages of DT
	7.10 Summary
	References
8 Cloud security-enabled digital twin in e-healthcare
	8.1 Introduction
	8.2 E-healthcare and cloud security-enabled digital twin
		8.2.1 ICT facilities
		8.2.2 Cloud security-enabled digital twin
	8.3 Cloud healthcare service platform with digital twin
		8.3.1 Wearable technologies
		8.3.2 Pharmaceutical industry
		8.3.3 Digital patients
		8.3.4 Hospital
	8.4 Security and privacy requirements for cloud security-enabled digital twin in e-healthcare
		8.4.1 Security requirements for cloud security-enabled digital twin in e-healthcare
		8.4.2 Privacy requirements for cloud security-enabled digital twin in e-healthcare
	8.5 Challenges in cloud-based digital twin in e-healthcare
	8.6 Conclusion
	References
9 Digital twin in prognostics and health management system
	9.1 Introduction
	9.2 Pile of DT
		9.2.1 Digital mirror (physical infrastructure)
		9.2.2 Digital data flow
		9.2.3 Digital virtual thread
	9.3 A complete DT model
	9.4 Phases of DT development
		9.4.1 Developing a simulation
		9.4.2 Fusion of data
		9.4.3 Interaction
		9.4.4 Service
	9.5 DT applications in healthcare
		9.5.1 Healthcare system
		9.5.2 Recovery of the patient
		9.5.3 Precision medicine
		9.5.4 Research in pharmaceutical development
		9.5.5 Drug administration
		9.5.6 Disease treating ways
	9.6 Challenges in DT implementation
		9.6.1 Infrastructure for information technology
		9.6.2 Data utilization
		9.6.3 Consistent modeling
		9.6.4 Modeling of domains
	9.7 Role of DT in healthcare
		9.7.1 Medicine that is tailored to the individual
		9.7.2 Development of virtual organs
		9.7.3 Medicine based on genomic data
		9.7.4 Healthcare apps
		9.7.5 Surgery scheduling
		9.7.6 Increasing the effectiveness of healthcare organizations
		9.7.7 Improving the experience of caregivers
		9.7.8 Increasing productivity
		9.7.9 Critical treatment window shrinking
		9.7.10 Healthcare delivery system based on value
		9.7.11 Rapid hospital erection
		9.7.12 Streamlining interactions in call center
		9.7.13 Development of pharmaceuticals and medical devices
		9.7.14 Detecting the dangers in drugs
		9.7.15 Simulating the new production lines
		9.7.16 Improving the device availability
		9.7.17 Post-sales surveillance
		9.7.18 Human variability simulation
		9.7.19 A lab’s DT
		9.7.20 Improving drug distribution
	9.8 Benefits
	References
10 Deep learning in Covid-19 detection and diagnosis using CXR images: challenges and perspectives
	10.1 Introduction
		10.1.1 CNN
		10.1.2 ANN
		10.1.3 RNN
		10.1.4 LSTM
		10.1.5 GRU
		10.1.6 Deep autoencoders
		10.1.7 Deep Boltzmann’s machine
	10.2 Related work
		10.2.1 Detection/localization
		10.2.2 Segmentation
		10.2.3 Registration
		10.2.4 Classification
		10.2.5 Application
	10.3 Proposed model
		10.3.1 Image pre-processing
		10.3.2 Data augmentation
		10.3.3 CNN with transfer learning
		10.3.4 ChestXRay20 dataset
	10.4 Experiments and result discussion
		Case 1: Covid-19 vs. healthy
		Case 2: Covid-19 vs. pneumonia
		Case 3: Normal vs. non-COVID
	10.5 Conclusions
	References
11 Case study: digital twin in cardiology
	11.1 Introduction
	11.2 Digital twin
	11.3 Issues in cardiology
	11.4 Digital twin heart
	11.5 Development of digital twin heart
	11.6 Philip’s HeartModelA.I
		11.6.1 Building the HeartModelA.I
		11.6.2 Image acquisition
		11.6.3 Phase detection
		11.6.4 Border detection
		11.6.5 Validation
		11.6.6 Tuning the model
		11.6.7 Uses of HeartModelA.I
	11.7 “Living Heart” Project
		11.7.1 Members of the “living heart”
	11.8 Impact of digital twin
		11.8.1 Organ simulation
		11.8.2 Genomic medicine
		11.8.3 Personalized health data
		11.8.4 Personalized treatment
		11.8.5 Improving the medical service
		11.8.6 Software-as-a-medical device
	11.9 Issues in using digital twin in healthcare
		11.9.1 Privacy issues
		11.9.2 Ethical issues
	11.10 Conclusion
	References
Index
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