Dental Composite Performance Prediction Using Artificial Intelligence.

ABSTRACT:

Objective

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 artificial intelligence (AI), specifically machine learning (ML), to predict the performance outcomes (POs) of dental composites from their composite attributes (CAs).

Methods

An 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.

Results

The KNN model excelled in predicting flexural modulus (FlexMod), Decision Tree model in flexural strength (FlexStr) and volumetric shrinkage (ShrinkV), and Logistic R

SUBMITTER: Rivera KP 

PROVIDER: S-EPMC11482990 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature