Title
AI-assisted image analysis of in vitro oral cell morphology for early disease detection
Authors
Nidhi Hirani1, Ketankumar Jayantilal Prajapati2, Ankita Shrivastava3, Aditya S. Dupare4,*, Nilesh Dinesh Pardhe5 & Rakashree Chakraborty6
Affiliation
1Department of Oral medicine and Radiology, Government Dental College and Hospital, Jamnagar, Gujarat, India; 2Department of Oral and Maxillofacial Pathology, Siddhpur Dental College and Hospital, Siddhpur, India; 3Department of Dentistry, Gajra Raja Medical College, Gwalior, Madhya Pradesh, India; 4Department of Oral Medicine diagnosis & Radiology, Yogita Dental College & Hospital, Khed, Dist Ratnagiri, Maharashtra, India; 5Department of Oral Pathology, ESIC Dental College & Hospital, Kalaburagi, Karnataka, India; 6Department of Oral Medicine and Radiology, Maharishi Markandeshwar College of Dental Sciences and Research, Mullana, Ambala, Haryana, India; *Corresponding author
Nidhi Hirani - E-mail: nidhihirani8@gmail.com
Ketankumar Jayantilal Prajapati - E-mail:
ketan.prajapati1987@gmail.com
Ankita Shrivastava - E-mail: dr.ankita2909@gmail.com
Aditya S. Dupare - E-mail: adupare@ymail.com
Nilesh Dinesh Pardhe - E-mail: drpardhenilesh@gmail.com
Rakashree Chakraborty - E-mail: drrakashreesen@gmail.com
Article Type
Research Article
Date
Received May 1, 2026; Revised May 31, 2026; Accepted May 31, 2026, Published May 31, 2026
Abstract
Premalignant and malignant oral lesions pose a significant global health burden, with delayed diagnosis adversely affecting patient outcomes. Therefore, it is of interest to evaluate the diagnostic performance and reliability of an artificial intelligence (AI)-based image analysis system in identifying early morphological changes in cultured oral epithelial cells under simulated pathological conditions. Human oral keratinocytes were exposed to oxidative stress and inflammatory cytokines and phase-contrast images were analyzed using a convolutional neural network trained to classify normal, stressed and dysplastic cells. The AI system demonstrated high accuracy (94.2%), sensitivity (93.8%), specificity (95.1%) and strong agreement with expert cytopathologists (κ = 0.91), with reduced analysis time compared to manual evaluation. AI-based image analysis shows promise as a reliable adjunctive tool for early detection and screening of oral lesions.
Keywords
Artificial intelligence (AI); oral epithelial cells; cell morphology; image analysis; early disease detection
Citation
Hirani et al. Bioinformation 22(5): 2817-2822 (2026)
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.
