In healthcare, a digital twin is a digital representation of a patient or healthcare system using integrated simulations and service data. The digital twin tracks a patient's records, crosschecks them against registered patterns and analyses any diseases or contra indications. The digital twin uses adaptive analytics and algorithms to produce accurate prognoses and suggest appropriate interventions. A digital twin can run various medical scenarios before treatment is initiated on the patient, thus increasing patient safety as well as providing the most appropriate treatments to meet the patient's requirements.
Digital Twin Technologies for Healthcare 4.0 discusses how the concept of the digital twin can be merged with other technologies, such as Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, IoT and cloud data management, for the improvement of healthcare systems and processes. The book also focuses on the various research perspectives and challenges in implementation of digital twin technology in terms of data analysis, cloud management and data privacy issues.
In the recent years, Digital Twin (DT) has gained a remarkable place in the top ten technology trends. But digital twinning alone cannot provide solution to many applications. Hence, the integration of DT with the technologies like Internet of Things (IoT), Artificial Intelligence (AI), Cloud Computing, Machine Learning (ML), Big Data analytics, and Deep Learning (DL) techniques paves way to new opportunities and provides a solution to many research problems in diverse sectors. The DT provides an accumulation of data between the real and digital system in both paths. This chapter focuses on the definition, architecture, components, and different types of DT. It also emphasizes on the convergence of digital twinning with other technologies for solving many research problems and application areas in medical diagnosis, healthcare, and others. It also highlights on the issues and challenges pertaining to DT and its supporting technologies. Finally, a case study pertaining to manufacturing sector using DT is presented.
With chapters on visualisation techniques, prognostics and health management, this book is a must-have for researchers, engineers and IT professionals in healthcare as well as those involved in using digital twin technology, AI, IoT and Big Data analytics for novel applications.
Author(s): Rajesh Kumar Dhanaraj, Santhiya Murugesan, Balamurugan Balusamy, Valentina E. Balas
Year: 2023
Language: English
Pages: 2289
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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