Title

Development of AI-based tools for assessing temporomandibular joint disorders using MRI images

Authors

Aditya Narayan Shukla¹, Vishwannath Hiremath^2,*, Vineet Vaibhav¹, Shiwangi Kumari¹, Bassam Alkhalifah³ & Pranay Yajurvedi⁴

Affiliation

¹Department of Oral & Maxillofacial Surgery, Babu Banarasi Das College of Dental Sciences, Uttar Pradesh, India; ²Department of Oral and Maxillofacial Surgery, (A Unit of Hiremath Hospitals Pvt Ltd) Vijayanagar, Banglore, India; ³Department of Radiology, College of Medicine, Qassim University,Buraydah, Saudi Arabia; ⁴Department of Oral and Maxillofacial Surgery, Pacific Dental College and Hospital, Udaipur, Rajasthan, India; *Corresponding author

Email

Aditya Narayan Shukla - E-mail: adityanarayanshukla22@gmail.com
Vishwannath Hiremath - E-mail: drhiremathhospitals@gmail.com
Vineet Vaibhav - E-mail: Vineetvaibhav1@gmail.com
Shiwangi kumari - E-mail: shiwangisingh2903@gmail.com
Bassam Alkhalifah - E-mail: b.alkhalifah@qu.edu.sa
Pranay Yajurvedi - E-mail: pranayyajurvedi1@gmail.com

Article Type

Research Article

Date

Received February 1, 2026; Revised February 28, 2026; Accepted February 28, 2026, Published February 28, 2026

Abstract

Temporomandibular joint disorders (TMDs) are diagnostically challenging due to the complexity of MRI interpretation and high inter-observer variability among clinicians. Therefore, it is of interest to develop and evaluate artificial intelligence–based tools for automated assessment of TMDs using magnetic resonance imaging. Hence, a total of 2,847 TMJ MRI examinations were used to train and test deep learning models for disc displacement classification, osteoarthritic change detection and joint effusion identification. The convolutional neural network achieved diagnostic accuracies of 94.2%, 91.8% and 93.5%, respectively, with area under the ROC curve values exceeding 0.92 and strong agreement with expert radiologists (κ = 0.87–0.91). The AI system reduced interpretation time by 68%, demonstrating its potential to improve diagnostic accuracy, consistency and efficiency in clinical TMJ evaluation.

Keywords

Artificial intelligence, temporomandibular joint disorders (TMDs), magnetic resonance imaging (MRI), deep learning, computer-aided diagnosis.

Citation

Shukla et al. Bioinformation 22(2): 695-701 (2026)

Edited by

Hiroj Bagde

ISSN

0973-2063

Publisher

Biomedical Informatics

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.