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CHEN Yafei, LIU Qiong, GUAN Shuang, YU Yanan, LIU Jun, WANG Sicun, WANG Zhong. The Method and Application of Digital Twinning in Medicine[J]. Medical Journal of Peking Union Medical College Hospital. doi: 10.12290/xhyxzz.2023-0157
Citation: CHEN Yafei, LIU Qiong, GUAN Shuang, YU Yanan, LIU Jun, WANG Sicun, WANG Zhong. The Method and Application of Digital Twinning in Medicine[J]. Medical Journal of Peking Union Medical College Hospital. doi: 10.12290/xhyxzz.2023-0157
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The Method and Application of Digital Twinning in Medicine

doi: 10.12290/xhyxzz.2023-0157

  • Institute of Basic Clinical Medicine of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100007, China

Funds:

China Academy of Chinese Medical Sciences Innovation fund (CI2021A04707)

  • Received Date: 2023-03-28
    Available Online: 2023-09-18
    Abstract
  • With the rapid development of emerging biotechnologies such as high-throughput sequencing, multi-omics and multi-dimensional research models for biological big data have been initiated. Concurrently, the fast-evolving technologies such as mathematical modeling, artificial intelligence, cloud computing, blockchain, big data, the Internet of Things, and 5G have enabled the possibility of developing digital twin. Digital twin is a model mapping of physical objects, processes, and systems in digital space, which has shown great potential in the medical field. On the one hand, digital twin technology can provide visualized 3D structures for human organs and systems to assist diagnosis and treatment. On the other hand, it provides a tangible "skeleton" for data mining in genomics, metabolomics, and phenomics, and can simulate processes for chronic disease management, drug development, and clinical trials, thereby advancing the medical field. This article reviewed the methods and applications of digital twin in the medical field, aiming to provide reference for the development of medical digital twin research in China.
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  • [1] Grieves MW. Product lifecycle management:the new paradigm for enterprises[J]. Int J Prod Dev, 2005, 2:71-84.
    [2] Renaudin CP, Barbier B, Roriz R, et al. Coronary arteries:new design for three-dimensional arterial phantoms[J]. Radiology, 1994, 190:579-582.
    [3] Tuegel EJ, Ingraffea AR, Eason TG, et al. Reengineering aircraft structural life prediction using a digital twin[J].Int J Aerospace Eng, 2011,2011:1-14.
    [4] Fang X, Wang H, Liu G, et al. Industry application of digital twin:from concept to implementation[J]. Int J Adv Manuf Tech, 2022, 121:4289-4312.
    [5] Glaessgen E, Stargel D. The digital twin paradigm for future NASA and US Air Force vehicles[C]. 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, 2012:1818.
    [6] West TD, Blackburn M. Is digital thread/digital twin affordable? A systemic assessment of the cost of DoD's latest manhattan project[J]. Procedia Comput Sci, 2017, 114:47-56.
    [7] Shafto M, Conroy M, Doyle R, et al. Modeling, simulation, information technology & processing roadmap[J]. NASA/TM, 2012, 32:1-38.
    [8] Sun T, He X, Song X, et al. The Digital Twin in Medicine:A Key to the Future of Healthcare?[J]. Front Med (Lausanne), 2022, 9:907066.
    [9] Saraeian S, Shirazi B. Digital twin-based fault tolerance approach for Cyber-Physical Production System[J]. ISA trans, 2022, 130:35-50.
    [10] Wright L, Davidson S. How to tell the difference between a model and a digital twin[J]. Adv Model Simul Eng Sci, 2020, 7:1-13.
    [11] Kim JK, Lee SJ, Hong SH, et al. Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer[J]. Appl Sci, 2022, 12:8156.
    [12] Hussain I, Hossain MA, Park SJ. A Healthcare Digital Twin for Diagnosis of Stroke[C]. IEEE, 2021:18-21.
    [13] Chakshu NK, Nithiarasu P. An AI based digital-twin for prioritising pneumonia patient treatment[J]. Proc Inst Mech Eng, Part H, 2022, 236:1662-1674.
    [14] González-Suárez A, Pérez JJ, Irastorza RM, et al. Computer modeling of radiofrequency cardiac ablation:30 years of bioengineering research[J]. Comput Meth Prog Bio, 2022, 214:106546.
    [15] Baumgartner C. The world's first digital cell twin in cancer electrophysiology:a digital revolution in cancer research?[J]. J Exp Clin Cancer Res, 2022, 41298.
    [16] Langthaler S, Rienmüller T, Scheruebel S, et al. A549 in-silico 1.0:A first computational model to simulate cell cycle dependent ion current modulation in the human lung adenocarcinoma[J]. PLoS Comput Biol, 2021, 17:e1009091.
    [17] Hoehme S, Hammad S, Boettger J, et al. Digital twin demonstrates significance of biomechanical growth control in liver regeneration after partial hepatectomy[J]. iScience, 2023, 26:105714.
    [18] Defraeye T, Bahrami F, Ding L, et al. Predicting transdermal fentanyl delivery using mechanistic simulations for tailored therapy[J]. Front Pharmacol, 2020, 11:585393.
    [19] Li X, Lee E J, Lilja S, et al. A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets[J]. Genome Med, 2022, 14:1-21.
    [20] Masison J, Beezley J, Mei Y, et al. A modular computational framework for medical digital twins[J]. Proc Natl Acad Sci USA. 2021;118:e2024287118.
    [21] de Jaegere P, De Santis G, Rodriguez-Olivares R, et al. Patient-specific computer modeling to predict aortic regurgitation after transcatheter aortic valve replacement[J]. JACC Cardiovasc Interv, 2016, 9:508-512.
    [22] Gray RA, Pathmanathan P. Patient-specific cardiovascular computational modeling:diversity of personalization and challenges[J]. J Cardiovasc Transl Res, 2018, 11:80-88.
    [23] Morrison TM, Dreher ML, Nagaraja S, et al. The role of computational modeling and simulation in the total product life cycle of peripheral vascular devices[J]. J Med Device, 2017, 11:024503.
    [24] Dillon-Murphy D, Noorani A, Nordsletten D, et al. Multi-modality image-based computational analysis of haemodynamics in aortic dissection[J]. Biomech Model Mechanobiol, 2016, 15:857-876.
    [25] Morris PD, van de Vosse FN, Lawford PV, et al. "Virtual"(computed) fractional flow reserve:current challenges and limitations[J]. JACC Cardiovasc Interv, 2015, 8:1009-1017.
    [26] Nørgaard B L, Leipsic J, Gaur S, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease:the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography:Next Steps)[J]. J Am Coll Cardiol, 2014, 63:1145-1155.
    [27] Zhou L, Meng X, Huang Y, et al. An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors[J]. Nat Mach Intell, 2022, 4:494-503.
    [28] Ahmed H, Devoto L. The potential of a digital twin in surgery[J]. Surg Innov, 2021, 28:509-510.
    [29] Chakshu NK, Nithiarasu P. An AI based digital-twin for prioritising pneumonia patient treatment[J]. Proc Inst Mech Eng H, 2022, 236:1662-1674.
    [30] Aubert K, Germaneau A, Rochette M, et al. Development of Digital Twins to Optimize Trauma Surgery and Postoperative Management. A Case Study Focusing on Tibial Plateau Fracture[J]. Front Bioeng Biotechnol, 2021, 9:722275.
    [31] D'Angelo E, Jirsa V. The quest for multiscale brain modeling[J]. Trends Neurosci, 2022, 45:777-790.
    [32] Thiong'o GM, Rutka JT. Digital Twin Technology:The Future of Predicting Neurological Complications of Pediatric Cancers and Their Treatment[J]. Front Oncol, 2022, 11:781499.
    [33] 张捷,钱虹,周宏远.数字孪生技术在社区老年人安全健康监测领域的应用探究[J].中国医疗器械杂志, 2019,43:410-413+421.
    [34] Pinton P. Computational models in inflammatory bowel disease[J]. Clin Transl Sci, 2022, 15:824-830.
    [35] Filippo MD, Damiani C, Vanoni M, et al. Single-cell Digital Twins for Cancer Preclinical Investigation[J]. Methods Mol Biol, 2020,2088:331-343.
    [36] Du XX, Liu MY, Sun YH. Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning[J]. Comput Intell Neurosci,2022, 2022:6003293.
    [37] Emmert-Streib F, Yli-Harja O. What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health[J]. Int J Mol Sci, 2022, 23:13149.
    [38] Walsh JR, Smith AM, Pouliot Y, et al. Generating digital twins with multiple sclerosis using probabilistic neural networks[J]. arXiv preprint arXiv, 2002, 02779.
    [39] Greenbaum D. Making Compassionate Use More Useful:Using real-world data, real-world evidence and digital twins to supplement or supplant randomized controlled trials[C]. Biocomputing 2021:Proceedings of the Pacific Symposium. 2020:38-49.
    [40] Barbiero P, Vinas Torne R, Lió P. Graph representation forecasting of patient's medical conditions:Toward a digital twin[J]. Front Genet, 2021, 12:652907.
    [41] Lin TY, Chiu SYH, Liao LC, et al. Assessing overdiagnosis of fecal immunological test screening for colorectal cancer with a digital twin approach[J]. NPJ Digital Medicine, 2023, 6:24.
    [42] Björnsson B, Borrebaeck C, Elander N, et al. Digital twins to personalize medicine[J]. Genome Med, 2020, 12:1-4.
    [43] Acosta JN, Falcone GJ, Rajpurkar P, et al. Multimodal biomedical AI[J]. Nat Med, 2022, 28:1773-1784.
    [44] 田硕.基于数字孪生的脑胶质瘤治疗辅助作业优化[D].石家庄:河北科技大学,2020.
    [45] 于洋,苗坤宏,李正.基于数字孪生的中药智能制药关键技术[J].中国中药杂志,2021,46:2350-2355.
    [46] Mittelstadt B. Near-term ethical challenges of digital twins[J]. J Med Ethics, 2021, 47:405-406.
    [47] Braun M. Represent me:please! towards an ethics of digital twins in medicine[J]. J Med Ethics, 2021, 47:394-400.
    [48] 田永林,陈苑文,杨静,等.元宇宙与平行系统:发展现状、对比及展望[J].智能科学与技术学报,2023,5:121-132.
    [49] 王飞跃.数字医生与平行医疗:从医疗知识自动化到系统化智能医学[J].协和医学杂志,2021,12:829-833.
    [50] 杨林瑶,陈思远,王晓等.数字孪生与平行系统:发展现状、对比及展望[J].自动化学报,2019,45:2001-2031.
    [51] 王飞跃.平行控制与数字孪生:经典控制理论的回顾与重铸[J].智能科学与技术学报,2020,2:293-300.
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