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Updated: Feb 4, 2026

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth
Published on: April 8, 2020
AI-powered finite element analysis for predicting fracture patterns in endodontically treated teeth restored with
Divya Batra1, Priyatam Maruti Karade2, Chirag R Vaniya3
1Department of Conservative dentistry and Endodontics, National Dental College and Hospital, Dera Bassi, Punjab, India.
Artificial intelligence (AI) combined with finite element analysis (FEA) significantly improves fracture prediction in endodontically treated teeth (ETT). This AI-enhanced FEA model offers better accuracy for selecting optimal post systems for ETT restoration.
Area of Science:
- Biomaterials Science
- Computational Mechanics
- Dental Materials
Background:
- Endodontically treated teeth (ETT) exhibit reduced fracture resistance due to structural compromise.
- Conventional finite element analysis (FEA) has limitations in accurately predicting fracture patterns in ETT.
Purpose of the Study:
- To evaluate an artificial intelligence (AI)-enhanced FEA model for predicting fracture patterns in ETT restored with various post materials.
- To compare the predictive accuracy of AI-enhanced FEA with conventional FEA.
Main Methods:
- Trained an AI model on 500 prior FEA simulations.
- Tested 120 maxillary premolars restored with fiberglass, carbon fiber, zirconia, and cast metal posts.
- Validated the AI-powered FEA model against experimental fracture resistance outcomes.
Main Results:
- AI-enhanced FEA demonstrated superior predictive accuracy (92.3%) compared to conventional FEA (76.8%).
- The AI model closely correlated with actual fracture initiation sites (r = 0.91).
- AI integration improved the prediction of fracture patterns in ETT.
Conclusions:
- Integration of AI with FEA significantly enhances fracture prediction accuracy in ETT.
- AI-powered FEA can guide clinicians in selecting optimal post systems for improved outcomes in ETT restoration.
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