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Machine Learning for Revision Joint Arthroplasty: A Systematic Review of Distinct Challenges, Current Performance,
Teja Yeramosu1,2, Logan K Laubach3, Raveena Joshi1
1VCU Health Department of Orthopaedic Surgery, Richmond, VA, USA.
Arthroplasty Today
|May 27, 2026
Summary
Machine learning (ML) models show promise for predicting outcomes in revision total joint arthroplasty. These algorithms can aid in risk stratification and outcome prediction, but require critical evaluation before clinical use.
Area of Science:
- Orthopedic Surgery
- Medical Informatics
- Data Science in Healthcare
Background:
- Revision total joint arthroplasty (TJA) faces high complication rates and significant economic burden.
- Machine learning (ML) presents a potential avenue to enhance outcomes in revision TJA procedures.
- Predictive modeling is crucial for improving patient care and resource management in revision TJA.
Purpose of the Study:
- To systematically review the application of ML models in predicting outcomes for revision total hip arthroplasty (rTHA) and revision total knee arthroplasty (rTKA).
- To assess the predictive performance and validation of ML algorithms in the context of revision TJA.
- To identify the common outcomes predicted by ML models in revision TJA studies.
Main Methods:
- A comprehensive literature search was performed across Ovid MEDLINE, Embase, and Web of Science in May 2024.
- Studies were screened using Covidence, with systematic reviews and non-ML or non-revision arthroplasty studies excluded.
- Thirteen studies met the inclusion criteria for the review.
Main Results:
- The reviewed studies focused on predicting patient complications (7 studies), inpatient status/length of stay (3 studies), and readmissions/discharge dispositions (2 studies).
- ML models demonstrated varying degrees of predictive success, with 48.3% in the excellent range and 36.7% in the acceptable range based on AUC.
- Eleven studies reported predictive success, with most models achieving acceptable to excellent performance; ten studies were internally validated, and three externally validated.
Conclusions:
- ML algorithms in revision TJA are challenged by data heterogeneity, sparsity, and outcome imbalance.
- These ML models offer utility for clinical risk stratification, predicting complications, length of stay, and discharge disposition.
- Critical assessment of ML algorithms is essential prior to their widespread clinical adoption in revision TJA.
