数字孪生技术在脊柱外科领域中的应用研究 现状
The Current Research Status of Digital Twin Technology in Spinal Surgery
DOI: 10.12677/acm.2026.1641371, PDF,    科研立项经费支持
作者: 者尚佳, 赵 智*, 王迎松, 张 立, 郝家豪:昆明医科大学第二附属医院骨科,云南 昆明
关键词: 脊柱/外科学人工智能精准医学数字孪生Spine/Surgery Artificial Intelligence Precision Medicine Digital Twin
摘要: 数字孪生技术通过将物理实体映射至虚拟空间,实现现实世界与数字世界的实时交互与反馈。近年来,该技术的应用已从工业领域逐步拓展至医疗健康领域,推动现代医学向“精准化、个性化、预测化”方向发展。本文系统综述数字孪生技术在脊柱外科中的应用现状,探讨其未来发展方向,并分析当前面临的技术、经济与伦理挑战。
Abstract: Digital twin technology projects physical entities into a virtual space, enabling real-time interaction between the real world and the digital environment. In recent years, its application has gradually expanded from industry to healthcare, promoting the development of modern medicine toward “precision, personalization, and prediction”. This article systematically reviews the current applications of digital twin technology in spinal surgery, envisions future directions, and analyzes the existing challenges in its development and implementation.
文章引用:者尚佳, 赵智, 王迎松, 张立, 郝家豪. 数字孪生技术在脊柱外科领域中的应用研究 现状[J]. 临床医学进展, 2026, 16(4): 1390-1396. https://doi.org/10.12677/acm.2026.1641371

参考文献

[1] Grieves, M. (2015) Digital Twin: Manufacturing Excellence through Virtual Factory Replication.
[2] Glaessgen, E. and Stargel, D. (2012) The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Honolulu, 23-26 April 2012. [Google Scholar] [CrossRef
[3] Tao, F., Zhang, H., Liu, A. and Nee, A.Y.C. (2019) Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15, 2405-2415. [Google Scholar] [CrossRef
[4] Bamba, H., Singh, G., John, J., Inban, P., Prajjwal, P., Alhussain, H., et al. (2024) Precision Medicine Approaches in Cardiology and Personalized Therapies for Improved Patient Outcomes: A Systematic Review. Current Problems in Cardiology, 49, Article ID: 102470. [Google Scholar] [CrossRef] [PubMed]
[5] Lomax, N., Vinjamuri, S., Vinjamuri, S., Franco, D., Schroeder, G. and Harrop, J. (2024) A Comprehensive Exploration of Digital Twinning in Spine Surgery. Clinical Spine Surgery, 38, 224-229. [Google Scholar] [CrossRef] [PubMed]
[6] Thangaraj, P.M., Benson, S.H., Oikonomou, E.K., Asselbergs, F.W. and Khera, R. (2024) Cardiovascular Care with Digital Twin Technology in the Era of Generative Artificial Intelligence. European Heart Journal, 45, 4808-4821. [Google Scholar] [CrossRef] [PubMed]
[7] Corral-Acero, J., Margara, F., Marciniak, M., Rodero, C., Loncaric, F., Feng, Y., et al. (2020) The “Digital Twin” to Enable the Vision of Precision Cardiology. European Heart Journal, 41, 4556-4564. [Google Scholar] [CrossRef] [PubMed]
[8] Maïzi, Y., Arcand, A. and Bendavid, Y. (2024) Digital Twin in Healthcare: Classification and Typology of Models Based on Hierarchy, Application, and Maturity. Internet of Things, 28, Article ID: 101379. [Google Scholar] [CrossRef
[9] 左锐, 王梦莹, 朱声荣, 张晨, 李维计虹. 基于数字孪生技术的医院智能展示分析平台建设实践探索[J]. 中国数字医学, 2022, 17(8): 65-69.
[10] Azam, M.A., Khan, K.B., Salahuddin, S., Rehman, E., Khan, S.A., Khan, M.A., et al. (2022) A Review on Multimodal Medical Image Fusion: Compendious Analysis of Medical Modalities, Multimodal Databases, Fusion Techniques and Quality Metrics. Computers in Biology and Medicine, 144, Article ID: 105253. [Google Scholar] [CrossRef] [PubMed]
[11] Xiong, X., Graves, S.A., Gross, B.A., Buatti, J.M. and Beichel, R.R. (2024) Lumbar and Thoracic Vertebrae Segmentation in CT Scans Using a 3D Multi-Object Localization and Segmentation CNN. Tomography, 10, 738-760. [Google Scholar] [CrossRef] [PubMed]
[12] Kawathekar, I.D., Areeckal, A.S. and Ⅴ, A. (2024) A Novel Deep Learning Pipeline for Vertebra Labeling and Segmentation of Spinal Computed Tomography Images. IEEE Access, 12, 15330-15347. [Google Scholar] [CrossRef
[13] Lyu, Y., He, X., Li, Z., Song, H. and Song, X. (2024) Developing a Biomechanics Integrated Digital Twin for Enhanced Real-Time Guidance in Lumbar Pedicle Screw Fixation Surgery. In: Fei, M.R., et al., Eds., Advancement in Computational Methods for Life Systems Modelling and Simulation, Springer, 3-17. [Google Scholar] [CrossRef
[14] Ninarello, D., Brambilla, G., Crivellaro, C. and La Barbera, L. (2025) Design and Verification of an Innovative Ceramic Lumbar Interbody Fusion Cage Using Digital Twins. Orthopaedic Proceedings, 107, Article No. 54. [Google Scholar] [CrossRef
[15] Andres, A., Roland, M., Wickert, K., Diebels, S., Stöckl, J., Herrmann, S., et al. (2025) Advantages of Digital Twin Technology in Orthopedic Trauma Surgery—Exploring Different Clinical Use Cases. Scientific Reports, 15, Article No. 19987. [Google Scholar] [CrossRef] [PubMed]
[16] Aubert, K., Germaneau, A., Rochette, M., Ye, W., Severyns, M., Billot, M., et al. (2021) Development of Digital Twins to Optimize Trauma Surgery and Postoperative Management. A Case Study Focusing on Tibial Plateau Fracture. Frontiers in Bioengineering and Biotechnology, 9, Article ID: 722275. [Google Scholar] [CrossRef] [PubMed]
[17] Widmer, J., Fasser, M., Croci, E., Spirig, J., Snedeker, J.G. and Farshad, M. (2020) Individualized Prediction of Pedicle Screw Fixation Strength with a Finite Element Model. Computer Methods in Biomechanics and Biomedical Engineering, 23, 155-167. [Google Scholar] [CrossRef] [PubMed]
[18] Rose, L.D., Bateman, G. and Ahmed, A. (2023) Clinical Significance of Cement Leakage in Kyphoplasty and Vertebroplasty: A Systematic Review. European Spine Journal, 33, 1484-1489. [Google Scholar] [CrossRef] [PubMed]
[19] Ahmadian, H., Mageswaran, P., Walter, B.A., Blakaj, D.M., Bourekas, E.C., Mendel, E., et al. (2022) A Digital Twin for Simulating the Vertebroplasty Procedure and Its Impact on Mechanical Stability of Vertebra in Cancer Patients. International Journal for Numerical Methods in Biomedical Engineering, 38, e3600. [Google Scholar] [CrossRef] [PubMed]
[20] Ahmadian, H., Mageswaran, P., Walter, B.A., Blakaj, D.M., Bourekas, E.C., Mendel, E., et al. (2022) Toward an Artificial Intelligence‐Assisted Framework for Reconstructing the Digital Twin of Vertebra and Predicting Its Fracture Response. International Journal for Numerical Methods in Biomedical Engineering, 38, e3601. [Google Scholar] [CrossRef] [PubMed]
[21] Liebmann, F., von Atzigen, M., Stütz, D., Wolf, J., Zingg, L., Suter, D., et al. (2024) Automatic Registration with Continuous Pose Updates for Marker-Less Surgical Navigation in Spine Surgery. Medical Image Analysis, 91, Article ID: 103027. [Google Scholar] [CrossRef] [PubMed]
[22] Sahu, M., Ishida, H., Connolly, L., Fan, H., Deguet, A., Kazanzides, P., et al. (2024) ENTRI: Enhanced Navigational Toolkit for Robotic Interventions. IEEE Transactions on Medical Robotics and Bionics, 6, 1405-1408. [Google Scholar] [CrossRef
[23] Holm, F., Ghazaei, G., Czempiel, T., Özsoy, E., Saur, S. and Navab, N. (2023) Dynamic Scene Graph Representation for Surgical Video. 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, 2-6 October 2023, 81-87. [Google Scholar] [CrossRef
[24] Gordon, A.M., Vatti, L. and Mont, M.A. (2025) Smart Knee Implants and Functional Outcome for Total Knee Arthroplasty. The Journal of Knee Surgery, 38, 397-402. [Google Scholar] [CrossRef] [PubMed]
[25] Khosravi, B., Rouzrokh, P., Maradit Kremers, H., Larson, D.R., Johnson, Q.J., Faghani, S., et al. (2022) Patient-Specific Hip Arthroplasty Dislocation Risk Calculator: An Explainable Multimodal Machine Learning-Based Approach. Radiology: Artificial Intelligence, 4, e220067. [Google Scholar] [CrossRef] [PubMed]
[26] Xu, Z., Zhang, J., Greenberg, J., Frumkin, M., Javeed, S., Zhang, J.K., et al. (2024) Predicting Multi-Dimensional Surgical Outcomes with Multi-Modal Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8, 1-30. [Google Scholar] [CrossRef
[27] Suresh, S., Stubbs, A., Pulling, G., Amiri, A., Izatt, M.T., Labrom, R.D., et al. (2025) Digital Twin for the Analog Scoliometer: Advancements in Telehealth for Paediatric Spine Deformity Care. Scientific Reports, 15, Article No. 41065. [Google Scholar] [CrossRef
[28] Hein, J., Giraud, F., Calvet, L., Schwarz, A., Cavalcanti, N.A., Prokudin, S., et al. (2024) Creating a Digital Twin of Spinal Surgery: A Proof of Concept. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, 17-18 June 2024, 2355-2364. [Google Scholar] [CrossRef
[29] Ghita, M., Copot, D., Birs, I.R., Muresan, C.I., Keyser, R.D., Neckebroek, M., et al. (2022) Uncertainty Minimization and Feasibility Study for Managing the Complex and Interacting Anesthesia-Hemodynamic System. 2022 IEEE 61st Conference on Decision and Control (CDC), Cancun, 6-9 December 2022, 6064-6069. [Google Scholar] [CrossRef
[30] Lonsdale, H., Gray, G.M., Ahumada, L.M., Yates, H.M., Varughese, A. and Rehman, M.A. (2022) The Perioperative Human Digital Twin. Anesthesia & Analgesia, 134, 885-892. [Google Scholar] [CrossRef] [PubMed]
[31] Diniz, P., Grimm, B., Garcia, F., Fayad, J., Ley, C., Mouton, C., et al. (2025) Digital Twin Systems for Musculoskeletal Applications: A Current Concepts Review. Knee Surgery, Sports Traumatology, Arthroscopy, 33, 1892-1910. [Google Scholar] [CrossRef] [PubMed]
[32] Drummond, D. and Coulet, A. (2022) Technical, Ethical, Legal, and Societal Challenges with Digital Twin Systems for the Management of Chronic Diseases in Children and Young People. Journal of Medical Internet Research, 24, e39698. [Google Scholar] [CrossRef] [PubMed]