Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears.

PURPOSE: The aim of this study is to develop a machine learning model to identify important clinical features related to rotator cuff tears (RCTs) using explainable artificial intelligence (XAI) for efficiently predicting outpatients with RCTs. METHODS: A retrospective review of a local clinical registry dataset was performed to include patients with shoulder pain and dysfunction who underwent questionnaires and physical examinations between 2019 and 2022. RCTs were diagnosed by shoulder arthroscopy. Six machine-learning algorithms (Stacking, Gradient Boosting Machine, Bagging, Random Forest, Extreme Gradient Boost (XGBoost), and Adaptive Boosting) were developed for the prediction. The performance of the models was assessed by the area under the receiver operating characteristic curve (AUC), Brier scores, and Decision curve. The interpretability of the predicted outcomes was evaluated using Shapley additive explanation (SHAP) values. RESULTS: A total of 1684 patients who completed questionnaires and clinical tests were included, and 417 patients with RCTs underwent shoulder arthroscopy. In six machining learning algorithms for predicting RCTs, the accuracy, AUC values, and Brier scores were in the range of 0.81-0.86, 0.75-0.92, and 0.15-0.19, respectively. The XGBoost model showed superior performance with accuracy, AUC, and Brier scores of 0.85(95% confidence interval, 0.82-0.87), 0.92 (95% confidence interval,0.90-0.94), and 0.15 (95% confidence interval,0.14-0.16), respectively. The Shapley plot showed the impact of the clinical features on predicting RCTs. The most important variables were Jobe test, Bear hug test, and age for prediction, with mean SHAP values of 1.458, 0.950, and 0.790, respectively. CONCLUSION: The machine learning model successfully identified important clinical variables for predicting patients with RCTs. In addition, the best algorithm was also integrated into a digital application to provide predictions in outpatient settings. This tool may assist patients in reducing their pain experience and providing prompt treatments. LEVEL OF EVIDENCE: Level III.