Predicting outcomes of nonsurgical retreatment with artificial intelligence: A multicenter retrospective study using logistic regression, random forest, and extreme gradient boosting

Background: Nonsurgical root canal retreatment is a complex procedure influenced by multiple clinical and procedural factors, making accurate prediction of healing outcomes difficult. Machine learning offers a potential approach to enhance prognostic precision and guide clinical decisions in endodontic retreatment. Materials and Methods: A retrospective multicenter dataset of 1100 patients treated between 2015 and 2023 was analyzed. Demographic, clinical, and treatment-related variables were collected. Logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost) models were developed using a 70:30 training-testing split with cross-validation. Model performance was assessed using accuracy, sensitivity, specificity, area under the curve-receiver operating characteristic (AUC-ROC), and Brier scores. Predictor importance was evaluated using Shapley Additive Explanations. Results: The overall healing rate of apical periodontitis was 82%. LR achieved moderate discrimination (AUC-ROC = 0.81), RF showed better performance (0.88), and XGBoost achieved the highest accuracy (0.91; Brier score = 0.12). Negative predictors included preoperative periapical lesions, molar teeth, poor restorations, posts/cores, and multiple prior treatments. Favorable healing was associated with the absence of lesions and good coronal restorations. Conclusions: Among the tested models, XGBoost demonstrated the highest prognostic accuracy for predicting healing following non-surgical root canal retreatment. Its strong predictive capability supports its potential application in clinical decision-making, warranting further prospective validation.