Leveraging digital twins for improved orthopaedic evaluation and treatment.

Autor: Dean MC; School of Medicine Mayo Clinic Alix School of Medicine Rochester Minnesota USA., Oeding JF; Department of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy University of Gothenburg Gothenburg Sweden., Diniz P; Department of Orthopaedic Surgery Centre Hospitalier de Luxembourg-Clinique d'Eich Luxembourg Luxembourg., Seil R; Department of Orthopaedic Surgery Centre Hospitalier de Luxembourg-Clinique d'Eich Luxembourg Luxembourg., Samuelsson K; Department of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy University of Gothenburg Gothenburg Sweden.
Jazyk: angličtina
Zdroj: Journal of experimental orthopaedics [J Exp Orthop] 2024 Nov 10; Vol. 11 (4), pp. e70084. Date of Electronic Publication: 2024 Nov 10 (Print Publication: 2024).
DOI: 10.1002/jeo2.70084
Abstrakt: Purpose: 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.
Methods: 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.
Results: 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.
Conclusion: 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 of Evidence: Level V.
Competing Interests: The authors declare no conflict of interest.
(© 2024 The Author(s). Journal of Experimental Orthopaedics published by John Wiley & Sons Ltd on behalf of European Society of Sports Traumatology, Knee Surgery and Arthroscopy.)
Databáze: MEDLINE
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