Title

Artificial intelligence in the diagnosis of temporomandibular joint disorders using cone-beam computed tomography (CBCT)

Authors

Sunil Kar¹, Gopal Srivastava², Nidhi Hirani³, Aditya S Dupare^4,*, Sameer Gupta⁵ & Souhardya Roy⁶

Affiliation

¹Department of Prosthodontics, IDS, Soa University, Bhubaneswar, Odisha, India; ²Department of Oral Medicine and Radiology, Government Dental College Thiruvananthapuram, Kerala, India; ³Department of Oral Medicine and Radiology, Dharmsinh Desai University, Chalali, Nadiad, Gujarat, India; ⁴Department of Oral Medicine Diagnosis & Radiology, Yogita Dental College & Hospital, Khed, Dist Ratnagiri, Maharashtra, India; ⁵Department of Oral & Maxillofacial Surgery, Inderprastha Dental College and Hospital, Sahibabad, Ghaziabad, Uttar Pradesh, India; ⁶Department of Oral Medicine and Radiology, Kalinga Institute of Dental Sciences, Bhubaneswar, Odisha, India; *Corresponding author

Email

Sunil Kar - E - mail: sunilkar888@gmail.com; Phone: +91 6371571297
Gopal Srivastava - E - mail: gopalsrivastava45@gmail.com; Phone: +91 9532486010
Nidhi Hirani - E - mail: nidhihirani8@gmail.com; Phone: +91 9099376158
Aditya S Dupare - E - mail: adupare@ymail.com; Phone: +91 9665559075
Sameer Gupta - E - mail: sameer.gupta83@yahoo.in; Phone: +91 9873941402
Souhardya Roy - E - mail: mailsouhardyaroy@gmail.com; Phone: +91 7908300070

Article Type

Research Article

Date

Received April 1, 2025; Revised April 30, 2025; Accepted April 30, 2025, Published April 30, 2025

Abstract

Temporomandibular joint disorders represent disorders which hinder the proper functioning of TMJ alongside causing pain-related problems. Therefore, it is of interest to analyse 150 CBCT scans using AI integration methods applied for TMD diagnosis. The AI-generated model displayed 92.4% accurate results and 90.8% sensitivity together with 93.7% specificity at a 0.95 AUC that matched radiologist agreement at κ = 0.89. The availability of AI diagnostics cut down diagnostic assessment time to deliver higher efficiency together with greater consistency. The future application of AI-assisted CBCT analysis appears promising yet needs additional verification steps before it becomes clinically available for broader medical use.

Keywords

Artificial intelligence, temporomandibular joint disorders (TMD), cone-beam computed tomography (CBCT), deep learning, TMJ diagnosis, radiology automation

Citation

Kar et al. Bioinformation 21(4): 805-808 (2025)

Edited by

Hiroj Bagde MDS, (PhD), PGDCR, PGDHHM, PGDL, PGDM

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.