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Updated: Jan 10, 2026

Author Spotlight: Implementing the Enhanced Recovery After Surgery Concept in Rehabilitation Following Anterior Cruciate Ligament Reconstruction
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Predicting return to sport after multiligament knee injuries using machine learning: development and internal

Carlos Suarez-Ahedo1, Francisco Endara-Urresta2, Carlos Peñaherrera-Carrillo3

  • 1Adult Hip and Knee Reconstruction Department. National Rehabilitation Institute of Mexico. Mexico City, Mexico.

The Knee
|November 28, 2025
PubMed
Summary

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A Clinical Decision-Making Algorithm for Posterolateral Corner Injuries of the Knee: Development and Internal Validation.

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Meniscal preservation in the age of biologics: toward a quantitative decision algorithm for personalized repair.

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Machine learning accurately predicts return to sport (RTS) after multiligament knee injuries (MLKIs). Key factors include preinjury activity, age, and timely surgery, aiding personalized rehabilitation.

Area of Science:

  • Orthopedic surgery
  • Sports medicine
  • Machine learning in healthcare

Background:

  • Multiligament knee injuries (MLKIs) require complex surgical reconstruction.
  • Predicting return to sport (RTS) is crucial for patient outcomes and rehabilitation planning.
  • Existing prediction methods may lack accuracy and comprehensive data integration.

Purpose of the Study:

  • Develop and validate a machine learning (ML) model to predict RTS at 12 months post-MLKI surgery.
  • Identify key clinical predictors influencing RTS after MLKI.
  • Compare the performance of different ML algorithms for RTS prediction.

Main Methods:

  • Retrospective analysis of 220 patients undergoing MLKI reconstruction (2012-2022).
  • Utilized demographic, clinical, surgical, and functional data for prediction.
Keywords:
Functional outcomeMachine learningMultiligament knee injuryPredictive modelingSHAP analysisSports medicine

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  • Compared logistic regression, SVM, random forest, and XGBoost models, validated with 10-fold cross-validation.
  • Employed SHAP analysis for feature importance interpretation.

    • Main Results:

    • 60.9% of patients achieved RTS at 12 months.
    • XGBoost model demonstrated superior predictive performance (AUC=0.84) compared to logistic regression (AUC=0.72).
    • Significant predictors for RTS included higher preinjury Tegner score, younger age, shorter time to surgery (<6 weeks), and higher baseline IKDC score.

    Conclusions:

    • Machine learning models offer accurate RTS prediction post-MLKI using readily available clinical data.
    • These predictive tools can personalize patient expectations and guide postoperative rehabilitation strategies.
    • The developed model supports informed clinical decision-making for MLKI recovery.