Dean Michael C, Oeding Jacob F, Diniz Pedro, Seil Romain, Samuelsson Kristian
School of Medicine Mayo Clinic Alix School of Medicine Rochester Minnesota USA.
Department of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy University of Gothenburg Gothenburg Sweden.
J Exp Orthop. 2024 Nov 10;11(4):e70084. doi: 10.1002/jeo2.70084. eCollection 2024 Oct.
The purpose of this article is to explore the potential of digital twin technologies in orthopaedics and to evaluate how their integration with artificial intelligence (AI) and deep learning (DL) can improve orthopaedic evaluation and treatment. This review addresses key applications of digital twins, including surgical planning, patient-specific outcome prediction, augmented reality-assisted surgery and simulation-based surgical training.
Existing studies on digital twins in various domains, including engineering, biomedical and orthopaedics are reviewed. We also reviewed advancements in AI and DL relevant to digital twins. We focused on identifying key benefits, challenges and future directions for the implementation of digital twins in orthopaedic practice.
The review highlights that digital twins offer significant potential to revolutionise orthopaedic care by enabling precise surgical planning, real-time outcome prediction and enhanced training. Digital twins can model patient-specific anatomy using advanced imaging techniques and dynamically update with real-time data, providing valuable insights during surgery and postoperative care. However, challenges such as the need for large-scale data sets, technological limitations and integration issues must be addressed to fully realise these benefits.
Digital twins represent a promising frontier in orthopaedic research and practice, with the potential to improve patient outcomes and enhance surgical precision. To enable widespread adoption, future research must focus on overcoming current challenges and further refining the integration of digital twins with AI and DL technologies.
Level V.
本文旨在探讨数字孪生技术在骨科领域的潜力，并评估其与人工智能（AI）和深度学习（DL）的整合如何改善骨科评估与治疗。本综述阐述了数字孪生的关键应用，包括手术规划、个性化患者预后预测、增强现实辅助手术以及基于模拟的手术训练。
对工程、生物医学和骨科等各领域中关于数字孪生的现有研究进行综述。我们还回顾了与数字孪生相关的人工智能和深度学习的进展。我们着重确定在骨科实践中实施数字孪生的关键益处、挑战及未来方向。
该综述强调，数字孪生通过实现精确的手术规划、实时预后预测和强化训练，为彻底改变骨科护理带来了巨大潜力。数字孪生可以利用先进的成像技术对患者特定的解剖结构进行建模，并通过实时数据动态更新，在手术和术后护理期间提供有价值的见解。然而，要充分实现这些益处，必须解决诸如需要大规模数据集、技术限制和整合问题等挑战。
数字孪生是骨科研究和实践中一个很有前景的前沿领域，具有改善患者预后和提高手术精度的潜力。为了实现广泛应用，未来的研究必须专注于克服当前的挑战，并进一步完善数字孪生与人工智能和深度学习技术的整合。
V级。
