Title

Artificial intelligence in diagnosis of maxillary sinusitis: A clinical study

Authors

Pranav V Manek¹, Kolasani Balaram², Sunil N Khot³, Heena Dixit⁴, Gunjan Modi⁵, Deepak Sharma⁴ & Rahul Tiwari^6,*

Affiliation

¹Department of Oral Medicine and Radiology, Pacific Dental College and Research Centre, Udaipur, Rajasthan, India, Boston University, GMS, Chobanian & Avedisian School of Medicine, Boston, MA, USA; ²Department of General Dentist, Aspen Dental, Manitowoc, Wisconsin, USA; ³Department of ENT, Government Medical College Nandurbar, Maharashtra, India; ⁴Department of Medical Health Administration, Index Institute, Malwanchal University, Index City, Nemawar Road, Indore, Madhya Pradesh, India; ⁵Department of Oral Medicine & Radiology, Pacific Dental College & Hospital, Udaipur, Rajasthan, India; ⁶Department of Oral and Maxillofacial Surgery, Narsinhbhai Patel Dental College and Hospital, Sankalchand Patel University, Visnagar, Gujarat, India; *Corresponding author

Email

Pranav V Manek - E-mail: drpranavmanek92@gmail.com
Kolasani Balaram - E-mail: khotsunil1@gmail.com
Heena Dixit - E-mail: khotsunil1@gmail.com
Gunjan Modi - E-mail: drgunjannmodi@gmail.com
Deepak Sharma- E-mail: heena16.d@gmail.com
Rahul Tiwari - E-mail: drrahulvctiwari@gmail.com

Article Type

Research Article

Date

Received July 1, 2025; Revised July 31, 2025; Accepted July 31, 2025, Published July 31, 2025

Abstract

Maxillary sinusitis is a common inflammatory condition often diagnosed through imaging modalities such as "cone-beam computed tomography (CBCT)". A prospective clinical research was conducted involving 200 patients with suspected maxillary sinusitis. The AI model achieved a sensitivity of 89.1%, specificity of 91.7%, positive predictive value of 92.5% and negative predictive value of 87.8% and overall accuracy of 90.4%. The area under the ROC curve was 0.948, indicating excellent diagnostic discrimination. AI demonstrates high reliability in diagnosing maxillary sinusitis on CBCT scans and may serve as an effective adjunct in clinical imaging interpretation, enhancing diagnostic efficiency and consistency.

Keywords

Artificial intelligence, maxillary sinusitis, CBCT imaging, diagnostic accuracy, deep learning

Citation

Manek et al. Bioinformation 21(7): 2108-2111 (2025)

Edited by

Rashmi Daga

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.