Title
AI-powered finite element analysis for predicting fracture patterns in endodontically treated teeth restored with posts
Authors
Divya Batra1, Priyatam Maruti Karade2,*, Chirag R. Vaniya3, Ataul Hafeez Imran4, Mohd Ahmed Ali Khan5 & Ishita Ghosh6
Affiliation
1Department of Conservative dentistry and Endodontics, National Dental College and Hospital, Dera Bassi, Punjab, India; 2Department of conservative dentistry and endodontics, Bharati Vidyapeeth (Deemed To Be University) Dental College and Hospital, Sangli, Maharashtra, India; 3Department of Prosthodontics and Crown & Bridge, Government Dental College and Hospital, Jamnagar, Jamnagar, India; 4Department of Conservative Dentistry and Endodontics, FICOI (USA), Fellowship Laser Dentistry (Wcli, USA) & Currently Practicing At Noor Hospital, Qadian, Gurdaspur, Punjab, India; 5Conservative Dentistry and Endodontics, Rungta college of dental science and research, Hyderabad, India; 6Department of Oral and Maxillofacial Surgery, Kalinga Institute of Dental Sciences, KIIT Deemed to be University, Bhubaneswar, Odisha, India; *Corresponding author
Divya Batra - E-mail: divyabatra88@gmail.com
Priyatam Maruti Karade - E-mail: priyatam.karade@bharatividyapeeth.edu
Chirag R. Vaniya - E-mail: chirag.vaniya011@gmail.com
Ataul Hafeez Imran - E-mail: imranqdn@gmail.com
Mohd Ahmed Ali Khan - E-mail: alikhan3548@gmail.com
Ishita Ghosh - E-mail: dr.ishitaghosh2001@gmail.com
Article Type
Research Article
Date
Received October 1, 2025; Revised October 31, 2025; Accepted October 31, 2025, Published October 31, 2025
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.
Keywords
Artificial intelligence, finite element analysis, endodontically treated teeth, fracture patterns, post systems, predictive modeling and dental biomechanics
Citation
Batra et al. Bioinformation 21(10): 3968-3972 (2025)
Edited by
Hiroj Bagde
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
