ArticleNPJ digital medicine2024
Identifying who are unlikely to benefit from total knee arthroplasty using machine learning models.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- CT-based 3D bone shape modes are associated with clinically meaningful functional improvement after total knee arthroplasty.Osteoarthritis and cartilage open · 2026Article
- Exploratory Spinopelvic Compensation Patterns Are Associated with a Strict Composite 12-Month MCID Endpoint After Total Knee Arthroplasty: A Retrospective Risk-Stratification Study.Medicina (Kaunas, Lithuania) · 2026Article
- Reply to determining MCID threshold for Knee Society Score to assess patient satisfaction in knee arthroplasty.NPJ digital medicine · 2026Article
- Explainable machine learning for orthopedic decision-making: predicting functional outcomes of total hip replacement from gait biomechanics.Arthritis research & therapy · 2025Article
- Determining MCID threshold for Knee Society Score to assess patient satisfaction in knee arthroplasty.NPJ digital medicine · 2025Article
- Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Article
- Artificial intelligence and multimodal imaging in orthopaedics: from technological advances to clinical translation.Frontiers in medicine · 2025Review
- Artificial intelligence in planned orthopaedic care.SICOT-J · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying and preventing patients who are not likely to benefit long-term from total knee arthroplasty (TKA) would decrease healthcare expenditure significantly. We trained machine learning (ML) models (image-only, clinical-data only, and multimodal) among 5720 knee OA patients to predict postoperative dissatisfaction at 2 years. Dissatisfaction was defined as not achieving a minimal clinically important difference in postoperative Knee Society knee and function scores (KSS), Short Form-36 Health Survey [SF-36, divided into a physical component score (PCS) and mental component score (MCS)], and Oxford Knee Score (OKS). Compared to image-only models, both clinical-data only and multimodal models achieved superior performance at predicting dissatisfaction measured by AUC, clinical-data only model: KSS 0.888 (0.866-0.909), SF-PCS 0.836 (0.812-0.860), SF-MCS 0.833 (0.812-0.854), and OKS 0.806 (0.753-0.859); multimodal model: KSS 0.891 (0.870-0.911), SF-PCS 0.832 (0.808-0.857), SF-MCS 0.835 (0.811-0.856), and OKS 0.816 (0.768-0.863). Our findings highlighted that ML models using clinical or multimodal data were capable to predict post-TKA dissatisfaction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.