Prediction of Electrophysiological Severity and Carpal Tunnel Syndrome Instrument Changes After Carpal Tunnel Release Using Machine Learning Model

Introduction: The severity of carpal tunnel syndrome (CTS) is evaluated by electrophysiological examination as well as a patient-oriented questionnaire. We hypothesized that machine learning could predict postoperative electrophysiological severity as well as the scores of patient-oriented questionnaires. In this study, we developed machine learning models to predict postoperative changes in electrophysiological severity and changes in the Carpal Tunnel Syndrome Instrument (CTSI). Materials and Methods: Data from four hundred and twenty hands of individuals who had been diagnosed with CTS and undergone carpal tunnel release were used. The features used for the machine learning model were preoperative age, gender, distal motor latency (DML) value, sensory nerve conduction velocity (SCV) value, preoperative electrophysiological severity stage, CTSI-SS value, and CTSI-FS value. Logistic Regression (LR), ElesticNet (EN), Support Vector Machine (SVM), Random Forest (RF), and LightGBM (LGBM) were used as machine learning algorithms. A machine learning model was created to binary classify the electrophysiologic severity at one year postoperatively. In the second experiment, regression models were created to predict the change in CTSI-SS and CTSI-FS at one year postoperatively. Results: In the electrophysiological severity classification model, LGBM showed the highest score (AUC = 0.802). Preoperative DML, age, and preoperative electrophysiological severity were important factors for model prediction. RF model showed the best performance. In the regression model predicting the change in CTSI-SS or CTSI-FS (RMSE: 0.418, 0.333, respectively), preoperative age and CTSI-SS or CTSI-FS scores were important factors for model prediction. Conclusions: The machine learning model can predict postoperative electrophysiological severity and CTSI score with high accuracy.