AI-powered finite element analysis for predicting fracture patterns in endodontically treated teeth restored with posts

TL;DR

This paper introduces an AI-enhanced finite element analysis model that improves the prediction of fracture patterns in teeth restored with posts after root canal treatment.

Contribution

The novel integration of AI with finite element analysis significantly improves fracture prediction accuracy in endodontically treated teeth.

Findings

AI-powered FEA achieved 92.3% predictive accuracy, outperforming conventional FEA at 76.8%.

The AI model closely correlated with actual fracture initiation sites (r = 0.91).

The model may guide clinicians in selecting optimal post systems for endodontically treated teeth.

Abstract

Endodontically treated teeth (ETT) are prone to fracture due to structural compromise and conventional finite element analysis (FEA)
has limitations in accurately predicting fracture behavior. Therefore, it is of interest to evaluate an artificial intelligence
(AI)-enhanced FEA model for predicting fracture patterns in ETT restored with fiberglass, carbon fiber, zirconia and cast metal posts.
Hence, a total of 120 maxillary premolars were tested, with the AI model trained on 500 prior FEA simulations and validated against
experimental fracture resistance outcomes. The AI-powered FEA showed superior predictive accuracy (92.3%) compared to conventional FEA
(76.8%) and closely correlated with actual fracture initiation sites (r = 0.91). Integration of AI with FEA enhances fracture prediction
and may guide clinicians in selecting optimal post systems for improved outcomes in ETT.