Leveraging digital twins for improved orthopaedic evaluation and treatment
Abstract 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 application...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Journal of Experimental Orthopaedics |
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| Online Access: | https://doi.org/10.1002/jeo2.70084 |
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| author | Michael C. Dean Jacob F. Oeding Pedro Diniz Romain Seil Kristian Samuelsson ESSKA Artificial Intelligence Working Group |
| author_facet | Michael C. Dean Jacob F. Oeding Pedro Diniz Romain Seil Kristian Samuelsson ESSKA Artificial Intelligence Working Group |
| author_sort | Michael C. Dean |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-3d321adae95241c9b54550bcbeb5e11c |
| institution | DOAJ |
| issn | 2197-1153 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Experimental Orthopaedics |
| spelling | doaj-art-3d321adae95241c9b54550bcbeb5e11c2025-08-20T02:58:21ZengWileyJournal of Experimental Orthopaedics2197-11532024-10-01114n/an/a10.1002/jeo2.70084Leveraging digital twins for improved orthopaedic evaluation and treatmentMichael C. Dean0Jacob F. Oeding1Pedro Diniz2Romain Seil3Kristian Samuelsson4ESSKA Artificial Intelligence Working GroupSchool of Medicine Mayo Clinic Alix School of Medicine Rochester Minnesota USADepartment of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy University of Gothenburg Gothenburg SwedenDepartment of Orthopaedic Surgery Centre Hospitalier de Luxembourg—Clinique d'Eich Luxembourg LuxembourgDepartment of Orthopaedic Surgery Centre Hospitalier de Luxembourg—Clinique d'Eich Luxembourg LuxembourgDepartment of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy University of Gothenburg Gothenburg SwedenAbstract 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.https://doi.org/10.1002/jeo2.70084artificial intelligenceaugmented realitydeep learningdigital twinorthopaedics |
| spellingShingle | Michael C. Dean Jacob F. Oeding Pedro Diniz Romain Seil Kristian Samuelsson ESSKA Artificial Intelligence Working Group Leveraging digital twins for improved orthopaedic evaluation and treatment Journal of Experimental Orthopaedics artificial intelligence augmented reality deep learning digital twin orthopaedics |
| title | Leveraging digital twins for improved orthopaedic evaluation and treatment |
| title_full | Leveraging digital twins for improved orthopaedic evaluation and treatment |
| title_fullStr | Leveraging digital twins for improved orthopaedic evaluation and treatment |
| title_full_unstemmed | Leveraging digital twins for improved orthopaedic evaluation and treatment |
| title_short | Leveraging digital twins for improved orthopaedic evaluation and treatment |
| title_sort | leveraging digital twins for improved orthopaedic evaluation and treatment |
| topic | artificial intelligence augmented reality deep learning digital twin orthopaedics |
| url | https://doi.org/10.1002/jeo2.70084 |
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