Digital twin systems for musculoskeletal applications: A current concepts review
Open accessAbstract
Digital twin (DT) systems, which involve creating virtual replicas of physical objects or systems, have the potential to transform healthcare by offering personalised and predictive models that grant deeper insight into a patient's condition. This review explores current concepts in DT systems for musculoskeletal (MSK) applications through an overview of the key components, technologies, clinical uses, challenges, and future directions that define this rapidly growing field. DT systems leverage computational models such as multibody dynamics and finite element analysis to simulate the mechanical behaviour of MSK structures, while integration with wearable technologies allows real‐time monitoring and feedback, facilitating preventive measures, and adaptive care strategies. Early applications of DT systems to MSK include optimising the monitoring of exercise and rehabilitation, analysing joint mechanics for personalised surgical techniques, and predicting post‐operative outcomes. While still under development, these advancements promise to revolutionise MSK care by improving surgical planning, reducing complications, and personalising patient rehabilitation strategies. Integrating advanced machine learning algorithms can enhance the predictive abilities of DTs and provide a better understanding of disease processes through explainable artificial intelligence (AI). Despite their potential, DT systems face significant challenges. These include integrating multi‐modal data, modelling ageing and damage, efficiently using computational resources and developing clinically accurate and impactful models. Addressing these challenges will require multidisciplinary collaboration. Furthermore, guaranteeing patient privacy and protection against bias is extremely important, as is navigating regulatory requirements for clinical adoption. DT systems present a significant opportunity to improve patient care, made possible by recent technological advancements in several fields, including wearable sensors, computational modelling of biological structures, and AI. As these technologies continue to mature and their integration is streamlined, DT systems may fast‐track medical innovation, ushering in a new era of rapid improvement of treatment outcomes and broadening the scope of preventive medicine. Level of Evidence : Level V.
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- Authors
- Diniz, P., Grimm, B., Garcia, F., Fayad, J., Ley, C., Mouton, C., Oeding, J. F., Hirschmann, M. T., Samuelsson, K., & Seil, R.
- Affiliations
- University of Lisbon (PT)
- Luxembourg Institute of Health (LU)
- University of Luxembourg (LU)
- Centre Hospitalier de Luxembourg (LU)
- University of Gothenburg (SE)
- Kantonsspital Baselland (CH)
- Corresponding author
- Diniz, Pedro
- ORCID
- Venue
- Knee Surgery, Sports Traumatology, Arthroscopy
- Publisher
- Wiley
- Published
- 2025-02-24
- Volume
- 33
- Issue
- 5
- Pages
- 1892-1910
- ISSN
- 0942-2056 (ISSN-L)
- 1433-7347
- OpenAlex
- W4407908364
- Semantic Scholar
- 71c9b6ff20e2044576de2bd9463e69e5a6dd0f45
- Cited by
- 60
- References
- 136
- Citations by year
- 2026: 39 · 2025: 20
- FWCI
- 49.99
- Type
- journal article
- Language
- en
- Open access
- green
- orbilu.uni.lu
- Licence
- Locations
- Knee Surgery Sports Traumatology Arthroscopy (publishedVersion)
- PubMed (publishedVersion)
- Open Repository and Bibliography (University of Luxembourg) (acceptedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- digital twin, musculoskeletal system, multibody dynamics, finite element analysis, wearable sensors, rehabilitation, joint mechanics, machine learning, explainable AI, patient privacy, regulatory adoption
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Artificial Intelligence, Computer Simulation, Humans, Musculoskeletal Diseases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-12.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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