Machine learning models for predicting return to sports after anterior cruciate ligament reconstruction: Physical performance in early rehabilitation

Abstract

Objective: Return to sports (RTS) after anterior cruciate ligament reconstruction (ACLR) is a crucial surgical success measure. In this study, we aimed to identify the best-performing machine learning models for predicting RTS at 12 months post-ACLR, based on physical performance variables at 3 months post-ACLR.

Methods: This case-control study included 102 patients who had undergone ACLR. The physical performance variables measured 3 months post-ACLR included the Biodex balance system, Y-balance test, and isokinetic muscle strength test. The RTS outcomes measured at 12 months post-ACLR included the single-leg hop test, single-leg vertical jump test, and Tegner activity score. Six machine learning algorithms were trained and validated using these data.

Results: Random forest models in the test set best predicted the RTS success based on the single-leg hop test (area under the curve [AUC], 0.952) and Tegner activity score (AUC, 0.949). Gradient boosting models in the test set best predicted the RTS based on the single-leg vertical jump test (AUC, 0.868).

Conclusion: Modifiable factors should be considered in the early rehabilitation stage after ACLR to enhance the possibility of a successful RTS.

Keywords: Exercise; machine learning; musculoskeletal; personalized medicine; rehabilitation; risk factors.

Figure 1.

Flowchart of patient selection.

Figure 2.

(a) Random forest model's feature permutation importance in the training set for predicting RTS-single leg hop test; (b) Shapley additive explanation analyses of random forest model in the training set for predicting RTS-single leg hop test; (c) ROC curves of six machine learning algorithms in the training set for predicting RTS-single leg hop test.