An explainable predictive model of direct pulp capping in carious mature permanent teeth

Objective: To introduce a novel approach for predicting the personalized probability of success of DPC treatment in carious mature permanent teeth using explainable machine learning (ML) models. Methods: Clinical data were obtained from our previous single-center retrospective study, comprising 393 carious mature permanent teeth from 372 patients who underwent DPC and attended 1-year follow-up between January 2015 and February 2021. Six ML models were derived based on 80 % cases of the cohort, with the remaining 20 % cases used for validation. Shapley additive explanation (SHAP) values were utilized to assess feature importance and the clinical relevance of prediction models. Results: Within the cohort, 9.67 % (38 out of 393) of teeth experienced failure at the 1-year follow-up after DPC treatment. Among the six evaluated ML models, the XGBoost model exhibited the highest discriminative ability. By prioritizing features based on their importance, streamlined and interpretable XGBoost model with 11 features were developed for 1-year prognostication post-DPC. The model demonstrated predictive accuracy with area under the curve (AUC) scores of 0.86 for the 1-year prediction. The final model has been translated into a web application to facilitate clinical decision-making. Conclusion: By incorporating demographic and clinical examination data, the XGBoost model offered a userfriendly tool for dentists to predict personalized probability of success, thereby improving personalized dental care and patient counseling. The utilization of SHAP for model interpretation provided transparent insights into the decision-making process.