Dental Composite Performance Prediction Using Artificial Intelligence

Abstract

There 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 the use of artificial intelligence (AI), specifically machine learning (ML) models, to predict the performance outcomes (POs) of dental composites from their composite attributes. A comprehensive dataset was carefully curated and refined from 200+ publications. Nine ML models were trained to predict discrete POs, and their performance was evaluated. Five models were used for regression analysis of continuous POs. Different ML models performed better on different POs. The k-nearest neighbors (KNN) model excelled in predicting flexural modulus (FlexMod), Decision Tree model in flexural strength (FlexStr) and volumetric shrinkage (ShrinkV), and Logistic Regression and Support Vector Machine models in shrinkage stress (ShrinkStr). Receiver-operating characteristic area under the curve 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 triethylene glycol dimethacrylate is a key contributor to FlexMod and ShrinkV, bisphenol A glycidyl dimethacrylate and urethane dimethacrylate to FlexStr, and depth of cure, degree of monomer-to-polymer conversion, and filler loading to ShrinkStr. There 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.