Prediction of marginal adaptation failure in restorations of non-carious cervical lesions, based on machine learning

Marginal adaptation failure in noncarious cervical restorations (NCCRS) significantly compromises restoration longevity and adversely impacts patient outcomes. early identification of high-risk restorations is therefore of clinical importance. This study aimed to develop and evaluate a supervised machine learning (ML) model capable of predicting the risk of marginal adaptation failure in NCCRS within 18 months following treatment. A total of 262 restorations were analyzed, incorporating multiple clinical variables, including adhesive system used, cavity geometry, degree of dentin sclerosis, incisogingival height, tooth characteristics, and patient age. Seven supervised ml algorithms were trained and assessed: decision tree, support vector machine (SVM), gradient boosting, k-nearest neighbors (KNN), logistic regression, multilayer perceptron, and random forest. model performance was evaluated using fivefold cross-validation and standard metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score. Key predictive features identified were incisogingival height, patient age, and type of adhesive system. the auc values ranged from 0.72 (95% confidence interval [95%CI]: 0.57-0.88) to 0.52 (95%CI: 0.51-0.83), and recall values ranged from 0.77 (95%CI: 0.66-0.89) to 0.53 (95%CI: 0.40-0.66). Among the tested algorithms, SVM, gradient boosting, and KNN demonstrated superior predictive performance. these findings suggest that ml models can serve as effective tools for predicting restoration failure and may assist clinicians in optimizing post-treatment monitoring and follow-up strategies for patients with NCCRS.