邮件订阅 RSS

留言板

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!

姓名
邮箱
手机号码
标题
留言内容
验证码

医学数字孪生方法及其应用

陈亚飞,  刘琼,  关双,  于亚南,  刘骏,  王思村,  王忠

downloadPDF
文章导航 > 协和医学杂志  > 2023 >  14(6): 1155-1161
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, 2023, 14(6): 1155-1161. 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, 2023, 14(6): 1155-1161. doi: 10.12290/xhyxzz.2023-0157
shu
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, 2023, 14(6): 1155-1161. 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, 2023, 14(6): 1155-1161. doi: 10.12290/xhyxzz.2023-0157
shu

医学数字孪生方法及其应用

doi: 10.12290/xhyxzz.2023-0157

  • 中国中医科学院中医临床基础医学研究所，北京 100007

基金项目: 

中国中医科学院科技创新工程 CI2021A04707

中国中医科学院重大攻关项目 CI2021A05033

详细信息
    通讯作者:

    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

  • 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

China Academy of Chinese Medical Sciences Innovation Fund CI2021A05033

More Information

    摘要
  • 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，以期为我国开展医学数字孪生研究提供参考。
    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 including mathematical modeling, artificial intelligence, cloud computing, blockchain, big data, the Internet of Things, and 5G have enabled the development of digital twin. Digital twin, a model mapping of physical objects, processes, and systems in digital space, has shown great potential in the medical field. Digital twin technology can provide visualized 3D structures for human organs and systems to assist diagnosis and treatment, create a tangible "skeleton" for data mining in genomics, metabolomics, and phenomics, and simulate processes for chronic disease management, drug development, and clinical trials, thereby advancing the medical field. The aim of the article, therefore, is to review the methods and applications of digital twin in the medical field, with the hope of providing reference for the development of medical digital twin research in China.
    作者贡献：陈亚飞、王思村负责文献整理、论文撰写；刘琼、关双、于亚南负责论文修订；王忠、刘骏负责论文撰写指导及修订。
    利益冲突：所有作者均声明不存在利益冲突
    • HTML全文
    • 参考文献(51)
  • [1] Grieves MW. Product lifecycle management: the new paradigm for enterprises[J]. Int J Prod Dev, 2005, 2: 71-84. doi:  10.1504/IJPD.2005.006669
    [2] Renaudin CP, Barbier B, Roriz R, et al. Coronary arteries: new design for three-dimensional arterial phantoms[J]. Radiology, 1994, 190: 579-582. doi:  10.1148/radiology.190.2.8284422
    [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. doi:  10.1007/s00170-022-09632-z
    [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. doi:  10.1016/j.procs.2017.09.003
    [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. doi:  10.1016/j.isatra.2022.03.007
    [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. doi:  10.1186/s40323-019-0138-7
    [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. doi:  10.3390/app12168156
    [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 H, 2022, 236: 1662-1674. doi:  10.1177/09544119221123431
    [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. doi:  10.1016/j.cmpb.2021.106546
    [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, 41: 298. doi:  10.1186/s13046-022-02507-x
    [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. doi:  10.1371/journal.pcbi.1009091
    [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. doi:  10.1016/j.isci.2022.105714
    [18] Defraeye T, Bahrami F, Ding L, et al. Predicting trans-dermal fentanyl delivery using mechanistic simulations for tailored therapy[J]. Front Pharmacol, 2020, 11: 585393. doi:  10.3389/fphar.2020.585393
    [19] Li X, Lee EJ, 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. doi:  10.1186/s13073-021-00995-8
    [20] Masison J, Beezley J, Mei Y, et al. A modular computa-tional framework for medical digital twins[J]. Proc Natl Acad Sci USA, 2021, 118: e2024287118. doi:  10.1073/pnas.2024287118
    [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. doi:  10.1016/j.jcin.2016.01.003
    [22] Gray RA, Pathmanathan P. Patient-specific cardiovascular computational modeling: diversity of personalization and challenges[J]. J Cardiovasc Transl Res, 2018, 11: 80-88. doi:  10.1007/s12265-018-9792-2
    [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. doi:  10.1115/1.4035866
    [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. doi:  10.1007/s10237-015-0729-2
    [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. doi:  10.1016/j.jcin.2015.04.006
    [26] Nørgaard BL, 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. doi:  10.1016/j.jacc.2013.11.043
    [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. doi:  10.1038/s42256-022-00483-7
    [28] Ahmed H, Devoto L. The potential of a digital twin in surgery[J]. Surg Innov, 2021, 28: 509-510. doi:  10.1177/1553350620975896
    [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. doi:  10.1177/09544119221123431
    [30] Aubert K, Germaneau A, Rochette M, et al. Development of Digital Twins to Optimize Trauma Surgery and Posto-perative Management. A Case Study Focusing on Tibial Plateau Fracture[J]. Front Bioeng Biotechnol, 2021, 9: 722275. doi:  10.3389/fbioe.2021.722275
    [31] D'Angelo E, Jirsa V. The quest for multiscale brain modeling[J]. Trends Neurosci, 2022, 45: 777-790. doi:  10.1016/j.tins.2022.06.007
    [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. doi:  10.3389/fonc.2021.781499
    [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. doi:  10.3969/j.issn.1671-7104.2019.06.005
    [34] Pinton P. Computational models in inflammatory bowel disease[J]. Clin Transl Sci, 2022, 15: 824-830. doi:  10.1111/cts.13228
    [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. doi:  10.3390/ijms232113149
    [38] Walsh JR, Smith AM, Pouliot Y, et al. Generating digital twins with multiple sclerosis using probabilistic neural networks[J/OL ]. https://doi.org/10.48550/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. doi:  10.3389/fgene.2021.652907
    [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. doi:  10.1038/s41746-023-00763-5
    [42] Björnsson B, Borrebaeck C, Elander N, et al. Digital twins to personalize medicine[J]. Genome Med, 2020, 12: 1-4. doi:  10.1186/s13073-019-0693-z
    [43] Acosta JN, Falcone GJ, Rajpurkar P, et al. Multimodal biomedical AI[J]. Nat Med, 2022, 28: 1773-1784. doi:  10.1038/s41591-022-01981-2
    [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. doi:  10.19540/j.cnki.cjcmm.20210114.601
    [46] Mittelstadt B. Near-term ethical challenges of digital twins[J]. J Med Ethics, 2021, 47: 405-406. doi:  10.1136/medethics-2021-107449
    [47] Braun M. Represent me: please! towards an ethics of digital twins in medicine[J]. J Med Ethics, 2021, 47: 394-400. doi:  10.1136/medethics-2020-106134
    [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. doi:  10.11959/j.issn.2096-6652.202313
    [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. doi:  10.12290/xhyxzz.2021-0586
    [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. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201911001.htm
    [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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNJS202003010.htm
    • 相关文章
    • 施引文献
  • 资源附件(0)
  • 加载中
WeChat 点击查看大图
计量
  • 文章访问数:  456
  • HTML全文浏览量:  71
  • PDF下载量:  104
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-03-28
  • 录用日期:  2023-05-22
  • 网络出版日期:  2023-09-14
  • 刊出日期:  2023-11-30

目录

    /

    返回文章
    返回
    友情链接: 国家卫生健康委员会 北京协和医院 中国医学科学院 学术不端检测系统 国际知识资源总库 中国知网 术语在线 中国全科医学
    地址：北京市东城区帅府园1号 北京协和医院老楼7号楼
    3层302室 《协和医学杂志》编辑部
    电话：010-69154261/4262
    E - mail：medj@pumch.cn
    mjpumch@126.com
    期刊网站版权所有：《协和医学杂志》编辑部
    京ICP备 11002662

    本系统由 北京仁和汇智信息技术有限公司 开发    百度统计

    x 关闭 永久关闭

    【温馨提醒】近日，《协和医学杂志》编辑部接到作者反映，有多名不法人员冒充期刊编辑发送见刊通知，鼓动作者添加微信，从而骗取版面费的行为。特提醒您，本刊与作者联系的方式均为邮件通知或电话，稿件进度通知邮箱为：mjpumch@126.com，编辑部电话为：010-69154261，请提高警惕，谨防上当受骗！如有任何疑问，请致电编辑部核实。谢谢！