Digital Twin - An Innovative Strategy in Healthcare Transformation: An Extensive Review
DOI:
https://doi.org/10.55544/jrasb.3.6.2Keywords:
Digital twin, Healthcare, Technology, PatientAbstract
In an age where the physical and digital worlds progressively intersect, the concept of the digital twin has aroused as a transformative force across various industries. Digital twins are dynamic digital imitations of physical objects; systems are procedures that can be used to simulate, analyse, and optimize their real-world analogue. In the health care field, a lot of work has gone into establishing digital twin of patient and medical devices. The digital twin of the patient is created by digitising the patient’s physical traits and bodily alterations. Real-world utilization of this technology includes accurate maintenance, advanced operational efficiency, and support for well-informed decision-making, all of which are trans-formative. The digital twin revolution is changing how healthcare professionals approach patient care, treatment planning, and facility administration. Digital twins provide instantaneous monitoring, personalized therapy, and predictive analytics by generating dynamic virtual replicas of patients, medical equipment, and healthcare systems. By offering insights on energy use, material consumption, and other vital variables, digital twin facilitates improved resource management and boosts businesses by cutting costs and waste. Digital twins are positioned to play a vital role in modern healthcare, inciting innovation and efficiency throughout the sector as technology advances. We focused on applications and development of digital twin in healthcare sector by analyzing a large number of studies from distinct medical sector, the effectiveness of digital twin in imaging studies and diagnosis, cancer, cardiology, neurology has been discussed in this review.
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Jun-Feng Yao, Yang Y, Wang, et al., Systematic Review of Digital Twin Technology and Applications. Visual Computing for Industry, Biomedicine, and Art. 2023; 6 (1): 1-20. https://doi.org/10.1186/s42492-023-00137-4.
Haghshenas A, Hasan A, Osen O, et al., Predictive Digital Twin for Offshore Wind Farms. Energy Inform. 2023; 6 (1): 1-26. https://doi.org/10.1186/s42162-023-00257-4.
The Increasing Potential and Challenges of Digital Twins. Nature Computational Science. 2024; 4 (3): 145–146. https://doi.org/10.1038/s43588-024-00617-4.
Sharma A, Kosasih E, Zhang J, et al., Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions. Journal of Industrial Information Integration. 2022; 30: 100383. https://doi.org/10.1016/j.jii.2022.100383.
Jeddoub I, Billen R, et al., Digital Twins for Cities: Analysing the Gap between Concepts and Current Implementations with a Specific Focus on Data Integration. International Journal of Applied Earth Observation and Geoinformation. 2023; 122 (10): 1-23. https://doi.org/10.1016/j.jag.2023.103440.
Piromalis D, Kantaros A. Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence. Applied System Innovation. 2022; 5 (4): 65. https://doi.org/10.3390/asi5040065.
Sun T, He X, et al., Digital Twin in Healthcare: Recent Updates and Challenges. Digital Health. 2023; 9. https://doi.org/10.1177/20552076221149651.
Holmes D, Papathanasaki M, Maglaras L, et al., Digital Twins and Cyber Security - Solution or Challenge? In 2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM); IEEE, 2021; 1–8. https://www.researchgate.net/publication/353917897
Pratt M K. 30 May 2024. 9 Advantages and disadvantages of digital twin technology. 08 August 2024. https://www.techtarget.com/searcherp/feature/Advantages-and-disadvantages-of-digital-twin-technology.
Howon Lee, Byungju Lee, Heecheol Yang, et al., Towards 6G Hyper-Connectivity: Vision, Challenges, and Key Enabling Technologies. Journal of Communications and Networks, 2023; 25 (3): 344-354.
Jun Zhang, Lin li, Guanjun Lin, et al., Cyber Resilience in Healthcare Digital Twin on Lung Cancer. IEEE Access, 2020; 8 (48): 5.
Jorge Corral-Acero, Francesca Margara, Maciej Marciniak et al., The 'Digital Twin' to enable the vision of precision cardiology. European Heart Journal. 2020; 41 (48): 4556–4564.
Eric J Topol. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 2019; 259 (1): 44-56.
Wei Zhou, Yan Jia, et al., The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved. IEEE Internet of Things Journal, 2019; 6 (2): 1606-1616.
Rachel Rabkin Peachman, April 12 2023. Meet America’s Best Management Consulting Firms.15 Mar 2023. https://www.forbes.com/sites/rachelpeachman/2023/03/15/meet-americas-best-management-consulting-firms-2023/
Pesapane F, Codari M, Sardanelli F. Artificial Intelligence in Medical Imaging: Threat or Opportunity? Radiologists Again at the Forefront of Innovation in Medicine. European Radiology Experimental. 2018; 2 (1):1-10. https://doi.org/10.1186/s41747-018-0061-6.
Croatti A, Gabellini M, Montagna S, et al., On the Integration of Agents and Digital Twins in Healthcare. Journal of Medical Systems. 2020; 44 (9): 1-8. https://doi.org/10.1007/s10916-020-01623-5.
Pesapane F, Rotili A, Penco S, et al., Digital Twins in Radiology. Journal of clinical medicine. 2022; 11 (21): 6553. https://doi.org/10.3390/jcm11216553.
Imran Ahmed, Awais Ahmad, et al., An IoT-Based Deep Learning Framework for Early Assessment of Covid-19. IEEE internet of things journal. 2021; 8 (21): 1-8.
Ahmed I, Jeon G. Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses. Interdisciplinary Sciences: Computational Life Sciences. 2022; 14 (2): 504–519. https://doi.org/10.1007/s12539-021-00465-0.
Ahmed I, Ahmad M, et al., Integrating Digital Twins and Deep Learning for Medical Image Analysis in the Era of COVID-19. Virtual Reality & Intelligent Hardware. 2022; 4 (4): 292–305. https://doi.org/10.1016/j.vrih.2022.03.002.
Pilati F, Tronconi R, Nollo G, et al., Digital Twin of COVID-19 Mass Vaccination Centres. Sustainability 2021; 13 (13): 1-26. https://doi.org/10.3390/su13137396.
Wang L, Wong A, et al., COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. Scientific reports. 2020; 10 (1):1-12. https://doi.org/10.1038/s41598-020-76550-z.
Muhammad Farooq, Abdul Hafeez. COVID-Res Net: A Deep Learning Framework for Screening of COVID19 from Radiographs. 2020; 1-5. https://arxiv.org/abs/2003.14395.
Pathak Y, Tiwari A, Stalin S, et al., Deep Transfer Learning Based Classification Model for COVID-19 Disease. IRBM 2022; 43 (2): 87–92. https://doi.org/10.1016/j.irbm.2020.05.003.
Singh D, Kumar V, Vaishali, et al., Classification of COVID-19 Patients from Chest CT Images Using Multi-Objective Differential Evolution-Based Convolutional Neural Networks. European Journal of Clinical Microbiology & Infectious Diseases. 2020; 39 (7): 1379–1389. https://doi.org/10.1007/s10096-020-03901-z.
Shamim Hossain M, Ghulam Muhammad, et al., Explainable AI and Mass Surveillance System-Based Healthcare Framework to Combat COVID-I9 like Pandemics. IEEE Network. 2020; 34 (4): 126–132. https://doi.org/10.1109/mnet.011.2000458.
Muhammad, G et ai., M. COVID-19 and non-COVID-19 Classification Using Multi-Layers Fusion from Lung Ultrasound Images, Information Fusion. 2021; 72: 80-88.https://doi.org/10.1016/j.inffus.2021.02.013.
Shorfuzzaman et al., MetaCOVID: A Siamese Neural Network Framework with Contrastive Loss for n-Shot Diagnosis of COVID-19 Patients. Pattern Recognit. 2021; 113 (107700): 107700. https://doi.org/10.1016/j.patcog.2020.107700.
Imran Ahmed et al., Internet of Health Things Driven Deep Learning-Based System for Non-Invasive Patient Discomfort Detection Using Time Frame Rules and Pairwise Key points Distance Feature. Sustain. Cities Soc. 2022; 79 (103672): 103672. https://doi.org/10.1016/j.scs.2022.103672.
Imran Ahmed et al., A Deep-Learning-Based Smart Healthcare System for Patient’s Discomfort Detection at the Edge of Internet of Things. IEEE Internet Things J. 2021; 8 (13): 10318–10326. https://doi.org/10.1109/jiot.2021.3052067.
Imran Ahmed et al., Automated Patient Discomfort Detection Using Deep Learning. Comput. Mater. Contin. 2022; 71 (2): 2559–2577. https://doi.org/10.32604/cmc.2022.021259.
Khagi B et al., Alzheimer’s disease Classification from BrainMRI based on transfer learning from CNN. In: 2018 11th Biomedical Engineering International Conference (BMEiCON), IEEE; 2018: p. 1–4. DOI:10.1109/BMEiCON.2018.8609974
Sultanpure et al., Internet of Things and Deep Learning Based Digital Twins for Diagnosis of Brain Tumour by Analysing MRI Images. Measurement Sensor. 2024; 33 (101220): 2665-9174. https://doi.org/10.1016/j.measen.2024.101220.
Hernandez-Boussard et al., Digital Twins for Predictive Oncology Will Be a Paradigm Shift for Precision Cancer Care. Nat Med. 2021; 27 (12): 2065–2066. https://doi.org/10.1038/s41591-021-01558-5.
Blair et al., Mathematical and Statistical Modelling in Cancer Systems Biology. Frontiers in Physiology. 2012, 3: 227. https://doi.org/10.3389/fphys.2012.00227.
Enderling et al., Mathematical Modelling of Tumour Growth and Treatment. Current Pharmaceutical Design. 2014; 20 (30): 4934–4940. https://doi.org/10.2174/1381612819666131125150434.
Chengyue et al., Integrating Mechanism-Based Modelling with Biomedical Imaging to Build Practical Digital Twins for Clinical Oncology. Biophysics Reviews. 2022; 3(2): 021304-23. https://doi.org/10.1063/5.0086789.
Ubels J et al., Predicting Treatment Benefit in Multiple Myeloma through Simulation of Alternative Treatment Effects. Nature Communications. 2018; 9 (1): 1–10.
https://doi.org/10.1038/s41467-018-05348-5.
Chaudhuri A et al., Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas. Frontiers in Artificial Intelligence. 2023; 6: 1-20. https://doi.org/10.3389/frai.2023.1222612.
Sarris A L, Sidiropoulos, et al., Towards a Digital Twin in Human Brain: Brain Tumor Detection Using K-Means. In Studies in Health Technology and Informatics. 2023; 302: 1052-1056. https://doi:10.3233/SHTI230345.
Eisenstein M. AI Assistance for Planning Cancer Treatment. Nature. 2024; 629 (8014): S14–S16. https://doi.org/10.1038/d41586-024-01431-8.
Stahlberg E A, Abdel-Rahman M et al., Exploring Approaches for Predictive Cancer Patient Digital Twins: Opportunities for Collaboration and Innovation. Front Digit Health. 2022; 4. https://doi.org/10.3389/fdgth.2022.1007784.
Keller J, Lindenmeyer A, et al., Using Digital Twins to Support Multiple Stages of the Patient Journey-In Studies in Health Technology and Informatics. IOS Press. 2023; 301: 227 - 232. https://doi.org/10.3233/SHTI230045.
Olivera-Salguero R, Segui E, et al., HOPE (SOLTI-1903) Breast Cancer Study: Real-World, Patient-Centric, Clinical Practice Study to Assess the Impact of Genomic Data on next Treatment Decision-Choice in Patients with Locally Advanced or Metastatic Breast Cancer. Frontiers Oncology. 2023; 13: 1-9 . https://doi.org/10.3389/fonc.2023.1151496.
Moztarzadeh O, Jamshidi M, et al., Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer. Bioengineering (Basel). 2023; 10 (4): 455. https://doi.org/10.3390/bioengineering10040455.
Meraghni S et al., Towards Digital Twins Driven Breast Cancer Detection. In Lecture Notes in Networks and Systems. Springer International Publishing Cham, 2021; 87–99.
Shen M et al., The Effectiveness of Digital Twins in Promoting Precision Health across the Entire Population: A Systematic Review. NPJ Digital Medicine. 2024; 7 (1): 1–10. https://doi.org/10.1038/s41746-024-01146-0.
Thangaraj P. M et al., A Novel Digital Twin Strategy to Examine the Implications of Randomized Control Trials for Real-World Populations. bioRxiv. 2024; 1-32. https://doi.org/10.1101/2024.03.25.24304868.
de Lepper A G W et al. From Evidence-Based Medicine to Digital Twin Technology for Predicting Ventricular Tachycardia in Ischaemic Cardiomyopathy. Journal Of the Royal Society Interface 2022; 19 (194): 1-15. https://doi.org/10.1098/rsif.2022.0317.
Mulder S T, Omidvari, et al., Steegers-Theunissen, R. Dynamic Digital Twin: Diagnosis, Treatment, Prediction, and Prevention of Disease during the Life Course. Med. Internet Res. 2022; 24 (9): e35675. https://doi.org/10.2196/35675.
Coorey G, Figtree G A , et al., The Health Digital Twin to Tackle Cardiovascular Disease-a Review of an Emerging Interdisciplinary Field. NPJ Digit Med. 2022; 5 (1).
https://doi.org/10.1038/s41746-022-00640-7.
Biancolini M E, Capellini K, et al., Fast Interactive CFD Evaluation of Hemodynamic Assisted by RBF Mesh Morphing and Reduced Order Models: The Case of a TAA Modelling. Int J Interact Des Manuf. 2020; 14 (4): 1227–1238. https://doi.org/10.1007/s12008-020-00694-5.
Martinez-Velazquez R, Gamez R, El Saddik A. Cardio Twin: A Digital Twin of the Human Heart Running on the Edge. IEEE. 2019; 1-6. https://doi.org/10.1109/MeMeA.2019.8802162.
Naplekov I, Zheleznikov I, et al., Methods of Computational Modeling of Coronary Heart Vessels for Its Digital Twin. MATEC Web Conf. 2018; 172: 01009. https://doi.org/10.1051/matecconf/201817201009.
Hirschvogel M, Jagschies L, et al., An in-Silico Twin for Epicardial Augmentation of the Failing Heart. Int J Numer Method Biomed Eng. 2019; 35 (10). https://doi.org/10.1002/cnm.3233.
Gillette K, Gsell M A F, et al., A Framework for the Generation of Digital Twins of Cardiac Electrophysiology from Clinical12-leads ECGs. Med Image Anal. 2021; 71 (102080): 1-18. https://doi.org/10.1016/j.media.2021.102080.
Hu Y, Chen J, et al., Personalized Heart Disease Detection via ECG Digital Twin Generation. arXiv [cs.LG]. 2024; https://doi.org/10.48550/arXiv.2404.11171 (accessed 2024-08-08).
Hussain I, Hossain M A, et al., A Healthcare Digital Twin for Diagnosis of Stroke. IEEE. 2021; 18–21. https://doi.org/10.1109/BECITHCON54710.2021.9893641.
Abirami L, Karthikeyan J. Digital Twin-Based Healthcare System (DTHS) for Earlier Parkinson Disease Identification and Diagnosis Using Optimized Fuzzy Based k-Nearest Neighbour Classifier Model. IEEE. 2023; 11: 96661–96672. https://doi.org/10.1109/ACCESS.2023.3312278.
Dang J, Lal A, et al., Developing DELPHI Expert Consensus Rules for a Digital Twin Model of Acute Stroke Care in the Neuro Critical Care Unit. BMC Neurol. 2023; 23 (1). https://doi.org/10.1186/s12883-023-03192-9.
Brown J W L, Coles A, et al., Association of Initial Disease-Modifying Therapy with Later Conversion to Secondary Progressive Multiple Sclerosis. JAMA. 2019; 321 (2): 175-187. https://doi.org/10.1001/jama.2018.20588.
Ghorbel E, Baptista R, et al., Home-Based Rehabilitation System for Stroke Survivors: A Clinical Evaluation. J Med Syst. 2020; 44 (12): 1-11. https://doi.org/10.1007/s10916-020-01661-z.
Stefano S. Digital Twin in Neuroscienc. J Neurosci. 2024;44(31). https://doi.org/10.1523/JNEUROSCI.0932-24.2024.
UCI machine learning repository. Uci.edu. https://archive.ics.uci.edu/ml/datasets/parkinsons (accessed 2024-08-08).
Cherubini V, Grimsmann J M, et al., Temporal Trends in Diabetic Ketoacidosis at Diagnosis of Paediatric Type 1 Diabetes between 2006 and 2016: Results from 13 Countries in Three Continents. Diabetologia. 2020; 63 (8): 1530–1541. https://doi.org/10.1007/s00125-020-05152-1.
Marin-Penalver J J, Martin-Timon I, et al., Update on the Treatment of Type 2 Diabetes Mellitus. World J Diabetes. 2016; 7 (17): 354-395. https://doi.org/10.4239/wjd.v7.i17.354.
Shamanna P, Mohammed J, et al., Retrospective Study of Glycaemic Variability, BMI, and Blood Pressure in Diabetes Patients in the Digital Twin Precision Treatment Program. Sci Rep. 2021; 11 (1): 1-9. https://doi.org/10.1038/s41598-021-94339-6.
Joshi S, Shamanna P, et al., MDigital Twin-Enabled Personalized Nutrition Improves Metabolic Dysfunction-Associated Fatty Liver Disease in Type 2 Diabetes: Results of a 1-Year Randomized Controlled Study. Endocr Pract. 2023; 29 (12): 960–970. https://doi.org/10.1016/j.eprac.2023.08.016.
Zeevi D, Korem T, et al., Personalized Nutrition by Prediction of Glycaemic Responses. Cell. 2015; 163 (5): 1079–1094. https://doi.org/10.1016/j.cell.2015.11.001.
Mendes-Soares H, Raveh-Sadka T, et al., Model of Personalized Postprandial Glycaemic Response to Food Developed for an Israeli Cohort Predicts Responses in Midwestern American Individuals. Am J Clin Nutr. 2019; 110 (1): 63–75. https://doi.org/10.1093/ajcn/nqz028.
Seo, Lee S, Park, et al., A Machine-Learning Approach to Predict Postprandial Hypoglycaemia. BMC Medical Information Decision Making. 2019; 19 (1): 1-13. https://doi.org/10.1186/s12911-019-0943-4.
Shamanna P, Erukulapati R.S, et al., One-Year Outcomes of a Digital Twin Intervention for Type 2 Diabetes: A Retrospective Real-World Study. Research Square. 2024; 1-20. https://doi.org/10.21203/rs.3.rs-4559618/v1.
Fuller A, Fan Z, et al., Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access. 2020; 8: 108952–108971. https://doi.org/10.1109/access.2020.2998358.
Shamanna P, Saboo B, et al., Reducing HbA1c in Type 2 Diabetes Using Digital Twin Technology-Enabled Precision Nutrition: A Retrospective Analysis. Diabetes Ther. 2020; 11 (11): 2703–2714. https://doi.org/10.1007/s13300-020-00931-w.
Thamotharan P, Srinivasan S, et al., Human Digital Twin for Personalized Elderly Type 2 Diabetes Management. J. Clin. Med. 2023; 12 (6): 2094. https://doi.org/10.3390/jcm12062094.
Wang S, Han J, et al., Development and Implementation of Patient-Level Prediction Models of End-Stage Renal Disease for Type 2 Diabetes Patients Using Fast Healthcare Interoperability Resources. Scientific Report. 2022; 12 (1): 11232. https://doi.org/10.1038/s41598-022-15036-6.
Sun T, Xiwang He, et al., Digital Twin in Healthcare: Recent Updates and Challenges. Digital Health-Sauge Journals. 2023; 9; 205520762211496. https://doi.org/10.1177/20552076221149651.
Ahmed H, Devoto L, et al., The Potential of a Digital Twin in Surgery. Surgical Innovation. 2021; 28 (4): 509–510. https://doi.org/10.1177/1553350620975896.
Bjelland O, Rasheed B, et al., Toward a Digital Twin for Arthroscopic Knee Surgery: A Systematic Review. IEEE Access. 2022; 10: 45029–45052. https;//doi.org/10.1109/ACCESS.2022.3170108
Abdallah Karakra, Franck Fontanili, et al., Pervasive Computing Integrated Discrete Event Simulation for a Hospital Digital Twin. IEEE Xplore. 2019; https://doi.org/10.1109/AICCSA.2018.8612796
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