Can a machine learning approach contribute to monitoring post-market surveillance of total knee arthroplasty prostheses?
Machine learning may offer a supplementary approach for the identification of prosthesis outliers. However, further analysis is required to fully comprehend the effect of confounding factors and the potential contribution of ML to the early identification of outliers.
Authors
- Mark Taylor (ORCID: https://orcid.org/0000-0001-7842-6472)
- Lucian B. Solomon (ORCID: https://orcid.org/0000-0001-6254-2372)
- Reza Hashemi (ORCID: https://orcid.org/0000-0002-5590-8409)
- Stephen E. Graves (ORCID: https://orcid.org/0000-0002-1629-319X)
- Khashayar Ghadirinejad (ORCID: https://orcid.org/0000-0002-4817-9973)
- Nicole Pratt (ORCID: https://orcid.org/0000-0001-8730-8910)
- Richard de Steiger (ORCID: https://orcid.org/0000-0002-1276-2040)
Institutions
- Royal Adelaide Hospital (AU)
- Flinders University (AU)
- University of South Australia (AU)
- Flinders Medical Centre (AU)
- Australian Orthopaedic Association (AU)
Publication Details
- Journal
- The Knee
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1016/j.knee.2025.10.020
- Primary Topic
- Total Knee Arthroplasty Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00