The influence of different factors on the bond strength of lithium disilicate-reinforced glass–ceramics to Resin: a machine learning analysis

Abstract

Background: To assess the influence of various factors on the bond strength of glass-based ceramics and develop a model that can predict the bond strength values using machine learning (ML). Methods: The bond strength values of lithium disilicate-reinforced glass–ceramics were collected from existing literature. Nineteen features were listed, and 9 ML algorithms, including logistic regression, k-nearest neighbors, support vector machine, decision tree, ensemble methods (extra trees, random forest, gradient boosting, and extreme gradient boosting), and multilayer perceptron, were employed. Importance analysis was performed to determine the significance of the 19 features. A new data set comprising the top five contributing features was used for bond strength class prediction. Grid search cross-validation (CV) and stratified tenfold CV were employed for hyperparameter tuning and model performance assessments. The evaluation metrics used were the area under the receiver operating characteristic curve (AUC) and accuracy. Nested CV was also employed to assess the model performance and avoid untruly optimistic results. Results: A total of 193 bond strength values were collected. Hydrofluoric acid concentration and etching time, gamma-methacryloxypropyltrimethoxysilane or 10-methacryloxydecyldihydrogen phosphate in the primer, and Bis-GMA in the cement were the top five features contributing to the bond strength. Stratified CV produced AUC scores of 0.71–0.93 and accuracy scores of 0.64–0.83. Extreme gradient boosting achieved superior model performance and accuracy and demonstrated good performance in predicting the range of bond strength values. Conclusions: ML shows promise as a data-driven tool for predicting the bond strength of glass-based ceramics to resin.