Prediction of Pulpal Sequelae in Cracked Teeth with Reversible Pulpitis using Machine Learning Models.

INTRODUCTION: In early stages of cracked teeth, pulpal inflammation is considered reversible. Cracked teeth with retained pulp vitality demonstrate higher survival rates whereas root canal treatment (RCT) negatively influences survival outcomes. The clinical challenge lies in discerning when RCT is required. This study aimed to develop and validate machine learning models for predicting pulp survival in cracked teeth with reversible pulpitis and investigating associations between patient- and tooth-related variables and treatment outcomes. METHODS: Data from 569 patients with 593 cracked teeth were analyzed using Logistic Regression, Gaussian Processes, Random Forests, and Gradient Boosting. Input features included age, gender, tooth type, and preoperative restorative material. The binary outcome represented pulp survival. A 10-fold stratified nested cross-validation approach was used: the outer loop estimated model performance and the inner loop optimized hyperparameters. Classification thresholds were tuned for probabilistic models. Performance metrics included area under the curve, F1-score, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: Logistic Regression achieved the highest area under the curve (0.64) and F1-score (0.60), with strong PPV (0.76) and moderate NPV (0.48), indicating effective identification of patients requiring RCT. Other models showed similar but slightly lower performance (PPV: 0.74-0.77; NPV: 0.45-0.47). Significant predictors of pulp survival included age and presence of preoperative restorations. Older patients and those with existing restorations were less likely to require RCT. CONCLUSIONS: Machine learning models demonstrated a predictive accuracy of 74%-77%. They could improve diagnostic precision and support timely decision-making for endodontic intervention in the management of cracked teeth with reversible pulpitis.