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|a DIGITAL TWIN FOR HEALTHCARE :
|b design, challenges and solutions.
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|a [S.l.] :
|b ELSEVIER ACADEMIC PRESS,
|c 2022.
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|a 1 online resource
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|a Digital Twins for Healthcare: Design, Challenges and Solutions establishes the state-of-art in the specification, design, creation, deployment and exploitation of digital twins' technologies for healthcare and wellbeing. A digital twin is a digital replication of a living or non-living physical entity. When data is transmitted seamlessly, it bridges the physical and virtual worlds, thus allowing the virtual entity to exist simultaneously with the physical entity. A digital twin facilitates the means to understand, monitor, and optimize the functions of the physical entity and provide continuous feedback. It can be used to improve citizens' quality of life and wellbeing in smart cities and the virtualization of industrial processes.
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|a Intro -- Title page -- Table of Contents -- Copyright -- Contributors -- Chapter 1: Introduction -- Abstract -- 1.1. History of digital twin -- 1.2. Elements of changes -- 1.3. The convergence of technologies -- 1.4. DT characteristics -- 1.5. Identify opportunities -- References -- Chapter 2: Underactuated digital twin's robotic hands with tactile sensing capabilities for well-being -- Abstract -- 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 -- Chapter 3: Digital twin for healthcare immersive services: fundamentals, architectures, and open issues -- Abstract -- 3.1. Introduction -- 3.2. Fundamentals of DT and XR -- 3.3. XR-DT-based system for healthcare requirements -- 3.4. XR-DT for healthcare architecture: emerging paradigms -- 3.5. Open issues -- 3.6. Learned lessons -- 3.7. Conclusion -- References -- Chapter 4: Challenges of Digital Twin in healthcare -- Abstract -- 4.1. Introduction -- 4.2. Representation -- 4.3. Sensing/actuating -- 4.4. Connectivity -- 4.5. Security, privacy, and ethical issues -- References -- Chapter 5: Intelligent digital twin reference architecture models for medical and healthcare industry -- Abstract -- 5.1. Introduction -- 5.2. Related work -- 5.3. Challenges -- 5.4. Digital twins models -- 5.5. DT architecture models -- 5.6. Case study: automatic remote surgeon using robot, DT and VR -- 5.7. Future direction -- References -- Chapter 6: Artificial intelligence models in digital twins for health and well-being -- Abstract -- 6.1. Background and introduction -- 6.2. AI in DT models -- 6.3. Types of AI models in DT for health -- 6.4. Discussion -- 6.5. Conclusion -- References.
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|a Chapter 7: COVIDMe: a digital twin for COVID-19 self-assessment and detection -- Abstract -- 7.1. Introduction -- 7.2. Computer-aided diagnosis -- 7.3. Digital twin -- 7.4. COVIDMe and the spread of COVID-19 -- 7.5. An overview of the COVIDMe software architecture -- 7.6. Discussion and future work -- 7.7. Conclusions -- References -- Chapter 8: Improving human living environment and human health through environmental digital twins technology -- Abstract -- 8.1. Introduction -- 8.2. Parameter identification and uncertainty estimation of the DTs model for central air-conditioning -- 8.3. Results and discussion -- 8.4. Conclusion -- References -- Chapter 9: Role of smart technologies in detecting cognitive impairment and enhancing assisted living -- Abstract -- Acknowledgements -- 9.1. Introduction -- 9.2. Mild cognitive impairment (MCI) detection -- 9.3. Providing assisted living -- 9.4. Conclusion -- References -- Chapter 10: Digital twins and cybersecurity in healthcare systems -- Abstract -- 10.1. Introduction -- 10.2. Digital twin opportunities in cyber security -- 10.3. Digital twin cyber security framework -- 10.4. Digital twin privacy framework -- 10.5. Digital twins compliance with standards and governance -- 10.6. Conclusion -- References -- Chapter 11: Potential applications of digital twin in medical care -- Abstract -- 11.1. Foundations for potential applications of digital twins in medical care -- 11.2. Applications of digital twin in medical care: state of the art -- 11.3. Future applications of digital twin in medical care -- References -- Chapter 12: Digital twins for decision support system for clinicians and hospital to reduce error rate -- Abstract -- 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.
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|a 12.3. What is digital twin for decision support system to reduce errors -- 12.4. Digital twin platform for decision support system to reduce errors -- 12.5. Digital twin system deployment, evaluation and operational consideration -- 12.6. Digital twin for decision support system challenges -- 12.7. Example case studies -- DSS -- 12.8. Conclusion -- References -- Chapter 13: Digital twin for cardiology -- Abstract -- Acknowledgements -- 13.1. Introduction to digital twin for cardiology -- 13.2. Digital twins to challenge heart disease -- 13.3. Digital twin for cardiology futures -- 13.4. Conclusion -- References -- Chapter 14: Applications of Digital Twins to migraine -- Abstract -- Acknowledgement -- 14.1. Introduction -- 14.2. Migraine disease -- 14.3. Digital Twins technology: definitions, required technologies and applications -- 14.4. Applications of Digital Twins Technology to migraine disease -- 14.5. Digital Twin solutions for migraine disease -- 14.6. Discussion -- 14.7. Conclusion -- References -- Chapter 15: Digital twins for nutrition -- Abstract -- Acknowledgement -- 15.1. Introduction -- 15.2. Related work -- 15.3. Research methodology -- 15.4. Documentation on DT and nutrition -- 15.5. Ecosystem of the digital twin for nutrition -- 15.6. Case study: hair loss -- 15.7. Discussion -- 15.8. Conclusion -- Clearly the lessons learned -- References -- Chapter 16: Digital twins for allergies -- Abstract -- Acknowledgement -- 16.1. Introduction -- 16.2. Related works -- 16.3. Ecosystem of the DT for allergy disease -- 16.4. Case study: anaphylaxis shocks -- 16.5. Discussion -- 16.6. Conclusion -- Clearly the lessons learned -- References -- Index.
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| 650 |
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0 |
|a Virtual reality in medicine.
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| 650 |
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0 |
|a Computer vision in medicine.
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| 650 |
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|a Medical care
|x Computer simulation.
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| 650 |
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|a Digital twins (Computer simulation)
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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|a Biological models.
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| 650 |
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|a Patient-Specific Modeling
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|a Artificial Intelligence
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| 650 |
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|a Models, Biological
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|a Computer Simulation
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|a Réalité virtuelle en médecine.
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| 650 |
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|a Vision par ordinateur en médecine.
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| 650 |
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|a Soins médicaux
|x Simulation par ordinateur.
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| 650 |
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|a Intelligence artificielle.
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| 650 |
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6 |
|a Modèles biologiques.
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| 650 |
|
7 |
|a artificial intelligence.
|2 aat
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|
7 |
|a Computer vision in medicine
|2 fast
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| 650 |
|
7 |
|a Digital twins (Computer simulation)
|2 fast
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| 650 |
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7 |
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| 655 |
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|a Electronic books.
|2 local
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| 776 |
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|i Print version:
|z 9780323950954
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|i Print version:
|z 0323991637
|z 9780323991636
|w (OCoLC)1289479866
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| 776 |
0 |
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|i Print version:
|a Saddik, Abdulmotaleb El
|t Digital Twin for Healthcare
|d San Diego : Elsevier Science & Technology,c2022
|z 9780323991636
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|a Texas A&M University
|b College Station
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|h Library of Congress classification
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| 998 |
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