Predictive accuracy of machine learning and markerless gait analysis for return-to-sport following lower extremity injury: a systematic review and meta-analysis

Background and objective Anterior cruciate ligament and lower extremity injuries impose a substantial burden in sports medicine, yet conventional assessments fail to capture the dynamic nature of injury risk and recovery. Machine learning and markerless gait analysis offer potential improvements in predicting return-to-sport readiness and re-injury risk, but their clinical reliability remains uncertain. This review evaluates the predictive accuracy and clinical applicability of these approaches. Methods A comprehensive search of PubMed, Embase, Scopus, IEEE Xplore, and CINAHL was conducted up to March 2026. Studies involving adults with lower extremity injuries using machine learning or markerless systems and reporting predictive metrics were included. Pooled accuracy estimates were calculated using a fixed-effect model with logit transformation. Heterogeneity was assessed using the I² statistic. Results Eleven studies met inclusion criteria for quantitative synthesis. For return-to-sport readiness (six wearable sensor studies), pooled accuracy was 0.92 (95% CI: 0.89–0.95; I ² = 52.2%). Sensitivity ranged from 0.79 to 0.99, and specificity from 0.75 to 0.99. For re-injury risk (two wearable sensor studies), pooled accuracy was 0.90 (95% CI: 0.84–0.95; I ² = 0.0%). Markerless motion analysis demonstrated sensitivity of 0.82 and specificity of 0.77 for injury risk screening. Deep learning and ensemble models outperformed traditional approaches. Conclusion Wearable sensor-based machine learning models achieve high predictive accuracy for return-to-sport and re-injury risk. However, methodological heterogeneity, reliance on internal validation, and absence of external validation limit generalisability. Standardised reporting and rigorous external validation are urgently needed before clinical implementation. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261352158 , identifier CRD420261352158.