FP8.5 Machine Learning for Prediction of Clinical Outcomes After Hip Arthroscopy: A Systematic Review

Abstract ICLA08 - AI and 3D Simulations: Trustworthy Clinical Partners or Tech Hype? Auditorium B (BMCC Level 1), October 8, 2026, 14:00 - 16:00 Background Machine learning (ML) has gained growing attention in orthopaedic surgery as a tool for personalized prediction, risk stratification, and surgical decision-making. In hip arthroscopy, where outcomes are influenced by multiple patient-specific, radiographic, psychological, and procedural factors, ML offers an attractive method for identifying complex patterns that may not be captured by traditional statistical models. Although several studies have applied ML to hip arthroscopy, the overall quality, consistency, and real world clinical value of these models remain unclear. Purpose To systematically review the current literature evaluating the use of machine learning to predict clinical outcomes, complications, revision risk, and resource utilization after hip arthroscopy. Methods A systematic search of PubMed, MEDLINE, and EMBASE was performed to identify studies applying machine-learning models to patients undergoing hip arthroscopy. Studies were included if they evaluated clinical prediction after hip arthroscopy, including patient-reported outcomes, complications, revision surgery, opioid use, or healthcare utilization. Attention was given during data extraction to discrimination, calibration, external validation, and comparison with traditional regression methods. Results Seventeen studies were included, with sample sizes varying widely from smaller institutional cohorts to larger database-based analyses. Most studies used supervised machine learning models, including logistic regression, random forest, gradient boosting, support vector machines, and neural networks. The main focus was on predicting patient-reported outcomes, clinically meaningful improvement, revision surgery, complications, and, in some cases, healthcare utilization. Model performance varies across studies. For patient-reported outcomes, most models showed moderate to good performance based on the area of under curve. Prediction of clinically meaningful improvement, including MCID and PASS, followed a similar pattern. In contrast, models predicting revision surgery and complications tended to show more modest accuracy. Studies looking at opioid use and healthcare utilization also reported moderate predictive performance. Across studies, several factors were consistently associated with poorer outcomes, including older age, higher body mass index, longer symptom duration, psychological comorbidities, worse baseline pain, and less favorable radiographic findings. However, external validation was limited, and reporting was not always consistent. Only a few studies directly compared machine learning with traditional statistical models, and overall, machine learning did not consistently demonstrate clear superiority. Conclusion Machine learning shows real promise for predicting outcomes after hip arthroscopy, particularly for patient-reported improvement, satisfaction, and selected perioperative risks. However, current models remain heterogeneous, incompletely validated, and not yet ready for routine clinical adoption in most settings. At present, ML appears more useful as a decision-support and research tool than as a standalone clinical solution. Future work should focus on external validation, standardized reporting, and development of generalizable models built from large, high-quality multicenter datasets.