Frontiers in sports and active living · 2026 · 系統性回顧＋統合分析（依標題推測）
Predictive accuracy of machine learning and markerless gait analysis for return-to-sport following lower extremity injury: a systematic review and meta-analysis
傷害流行病學・預防・篩檢 · 回場評估
中文閱讀摘要
統合分析顯示穿戴式機器學習預測重返運動準確率高，但缺乏外部驗證，臨床推廣證據有限。
AI 依來源摘要整理，未經人工逐篇核對。閱讀結論前，請回溯研究族群、方法與限制。
原始摘要
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.
至 Europe PMC 閱讀完整來源 ↗ · 擷取日期 2026-10-19
書目、來源與全文狀態
- DOI
- 10.3389/fspor.2026.1862870
- PMID／PMCID
- 42382450 / PMC13314457
- 作者
- Satija R, Dkhar D
- 首次發表日期
- 2026-06-16Europe PMC 提供，部分日期可能依年份或月份推定。
- 卷／期／頁碼或文章編號
- 8 ／ 期未提供 ／ 1862870
- 書目來源與查核日期
- Europe PMC · 2026-10-19僅核對書目欄位與來源連結；不代表已評讀研究品質。
- 全文與授權
- 來源列有免費全文（查核 2026-10-19），未逐篇測試 PDF 下載。 來源授權標示：cc by。
可回溯引用
Satija R, Dkhar D. Predictive accuracy of machine learning and markerless gait analysis for return-to-sport following lower extremity injury: a systematic review and meta-analysis. Frontiers in sports and active living. 2026;8:1862870. PMID: 42382450. PMCID: PMC13314457. doi:10.3389/fspor.2026.1862870
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