Abstract
Objectives
To evaluate the predictive performance of different machine learning algorithms for the occurrence of objective failure in ACL reconstruction and to identify the most relevant predictors associated with this outcome.
Methods
We evaluated 680 patients submitted submitted to ACL reconstruction between January 2012 and July 2021. The study outcome was ACL objective failure, defined as a complete tear confirmed by MRI or arthroscopy evaluation or clinically ACL insufficiency. Routinely collected imaging, clinical, and demographic data were used to train 9 machine learning algorithms (k-nearest neighbors (KNN) classifier, decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), CatBoost classifier, and logistic regression). We used a random sample of 70% of patients to train the algorithms, and 30% were left for performance assessment, simulating new data. The performance of the models was evaluated with the area under the receiver operating characteristic curve (AUC).
Results
The predictive performance of all models was good, with AUC’s ranging from 0.81 to 0.88. The models with the best AUC metric were the Gradient Boosting Classifier (0.88 [95% CI, 0.81 to 0.92]) and Random Forest Classifier (0.88 [ 95% CI, 0.81 to 0.92). Knee hyperextension consistently emerged as the primary predictor across all models subjected to our analysis.
Conclusion
Machine learning algorithms demonstrated good performance to predict ACL reconstruction objective failure. Additionally, knee hyperextension consistently emerged as the primary predictor across all models subjected to our analysis.