Dental Composite Performance Prediction Using Artificial Intelligence

ObjectiveThere is a need to increase the performance and longevity of dental composites and accelerate the translation of novel composites to the market. This study explores artificial intelligence (AI), specifically machine learning (ML), to predict the performance outcomes (POs) of dental composites from their composite attributes (CAs). MethodsAn extensive dataset from over 200 publications was built and refined to 233 samples with 17 CAs and 7 POs. Nine ML models were evaluated for PO prediction performance using classified data, and Five ML models were evaluated for PO regression analysis. ResultsThe KNN model excelled in predicting flexural modulus (FlexMod), Decision Tree model in flexural strength (FlexStr) and volumetric shrinkage (ShrinkV), and Logistic Regression and SVM models in shrinkage stress (ShrinkStr). Receiver operating characteristic area under the curve (ROC AUC) analysis confirmed these results but found that Random Forest was more effective for FlexStr and ShrinkV, suggesting the possibility of Decision Tree overfitting the data. Regression analysis revealed that the Voting Regressor was superior for FlexMod and ShrinkV predictions, while Decision Tree Regression was optimal for FlexStr and ShrinkStr. Feature importance analysis indicated TEGDMA is a key contributor to FlexMod and ShrinkV, BisGMA and UDMA to FlexStr, and depth of cure, degree of monomer-to-polymer conversion, and filler loading to ShrinkStr. SignificanceThere is a need to conduct a full analysis using multiple ML models because different models predict different POs better, and for a large, comprehensive dataset to train robust AI models to facilitate the prediction and optimization of composite properties and support the development of new dental materials.