Artificial intelligence and machine learning in knee arthroplasty

Artificial intelligence (AI) and its subset, machine learning (ML), have significantly impacted clinical medicine, particularly in knee arthroplasty (KA). These technologies utilize algorithms for tasks such as predictive analytics and image recognition, improving preoperative planning, intraoperative navigation, and postoperative complication anticipation. This systematic review presents AI-driven tools' clinical implications in total and unicompartmental KA, focusing on enhancing patient outcomes and operational efficiency.

A systematic search was conducted across multiple databases including Cochrane Central Register of Controlled Trials, Embase, OVID Medline, PubMed, and Web of Science, following the PRISMA guidelines for studies published in the English language till March 2024. Inclusion criteria targeted adult human models without geographical restrictions, specifically related to total or unicompartmental KA.

A total of 153 relevant studies were identified, covering various aspects of ML application for KA. Topics of studies included imaging modalities (n = 28), postoperative primary KA complications (n = 26), inpatient status (length of stay, readmissions, and cost) (n = 24), implant configuration (n = 14), revision (n = 12), patient-reported outcome measures (PROMs) (n = 11), function (n = 11), procedural communication (n = 8), total knee arthroplasty/unicompartmental knee arthroplasty prediction (n = 6), outpatient status (n = 4), perioperative efficiency (n = 4), patient satisfaction (n = 3), opioid usage (n = 3). A total of 66 ML models were described, with 48.7% of studies using multiple approaches.

This review assesses ML applications in knee arthroplasty, highlighting their potential to improve patient outcomes. While current algorithms and AI show promise, our findings suggest areas for enhancement in predictive performance before widespread clinical adoption.