Machine Learning in the Analysis of Hip Osteoarthritis and Total Hip Arthroplasty Gaits: A Systematic Review

The accurate diagnosis of Hip Osteoarthritis (HOA) and the prediction of Total Hip Arthroplasty (THA) outcomes are crucial for reliable decision-making on treatment and rehabilitation strategies. Gait analysis (GA) is commonly employed for gait disorder examination in clinical settings, but it is still limited due to the massive data size and accuracy problems. A Machine Learning (ML) methodology has seen rapid growth in the past decade, but its development in the context of HOA and THA GA has not been previously examined. Thus, the novel contribution of this review is the evaluation of the current state of ML frameworks for the analysis of HOA and post-THA gaits. Five databases, namely PubMed, Embase, IEEE Xplore, ACM Digital Library, and Scopus, were searched in accordance with the PRISMA framework. Relevant publications published until May 2025 were retrieved, and information on reliability, applicability, and interpretability were extracted for quality assessment. Out of the 759 publications initially considered, 19 studies were selected, with 14 articles focused on classification and 5 articles on outcome prediction. Eight classification studies utilized kinematic features, while four outcome prediction articles utilized spatiotemporal parameters and mostly focused on post-THA gaits. The reported accuracy ranges between 70 and 100%, with the support vector machine (SVM) as the most frequently utilized ML algorithm. Scarce datasets, small sample sizes, and limited design description were the main hindrances revealed in our quality assessment. Nevertheless, this review demonstrated the recent developments in the utilization of ML techniques and evidently improved applicability through a consensus on the important gait features for HOA and post-THA gait analysis. Reliability and interpretability are still major concerns before ML models become widely accepted by medical practitioners. Future research should consider dataset quality, transparent validation protocol, model interpretability, and results’ explainability.