尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!



陈亚飞 刘琼 关双 于亚南 刘骏 王思村 王忠

陈亚飞, 刘琼, 关双, 于亚南, 刘骏, 王思村, 王忠. 医学数字孪生方法及其应用[J]. 协和医学杂志. doi: 10.12290/xhyxzz.2023-0157
引用本文: 陈亚飞, 刘琼, 关双, 于亚南, 刘骏, 王思村, 王忠. 医学数字孪生方法及其应用[J]. 协和医学杂志. doi: 10.12290/xhyxzz.2023-0157
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


doi: 10.12290/xhyxzz.2023-0157



    王忠, E-mail:zhonw@vip.sina.com

  • 中图分类号: R-05;TP3

The Method and Application of Digital Twinning in Medicine


China Academy of Chinese Medical Sciences Innovation fund (CI2021A04707)

  • 摘要: 随着高通量测序等新兴生物技术的快速发展,多组学、多维度的生物大数据研发模式揭开序幕,同时数学建模、人工智能、云计算、区块链、大数据、物联网和 5G 等技术的快速迭代为数字孪生的发展提供了可能。数字孪生是对物理对象、流程和系统在数字空间的模型映射,在医学领域展现出巨大的发展前景:(1) 为人体器官及系统提供可视化的三维立体构象, 可辅助临床诊断和治疗;(2) 为基因组学、代谢组学、表型组学等数据挖掘提供有血有肉的"骨架";(3) 对于慢病管理、药物开发和临床试验等流程进行系统模拟, 从而推动医学事业的发展。本文通过梳理数字孪生在医学领域中的方法和应用,以期为我国发展医学数字孪生研究提供参考。
  • [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.
  • 加载中
  • 文章访问数:  12
  • HTML全文浏览量:  4
  • PDF下载量:  2
  • 被引次数: 0
  • 收稿日期:  2023-03-28
  • 网络出版日期:  2023-09-18