Title
AI-assisted versus traditional radiographic interpretation for early caries detection
Authors
Vikram Karande1,*, Mohd Zeeshan Ahmad2, Vishal Kulkarni3, Priyanka Chandra4, Sowjanya Gunukula5, Mousami Kundu6 & Ritik Kashwani7
Affiliation
1Department of Dentistry, AIIMS, Rajkot, Gujarat, India; 2Department of Conservative Dentistry & Endodontics, Rama Dental College, Hospital & Research Centre, Kanpur, Uttar Pradesh, India; 3Department of Oral and Maxillofacial Surgery, Army Dental Centre Research and Referral, Delhi Cantt, India; 4Department of Conservative Dentistry and Endodontics, Kothiwal Dental college and Research Centre Kanth Road, Moradabad, Uttar Pradesh, India; 5Private Partition, Flomo Dental Private Clinic 295 Ukiah St, Lewisville, Texas, USA; 6Department of Oral & Maxillofacial Surgery, Awadh Dental College and Hospital, NH-33, Dalma, Bhilaipahari, Dalma, Jamshedpur, Jharkhand, India; 7Department of Oral Medicine and Radiology, School of Dental Sciences, Sharda University, Greater Noida, Uttar Pradesh, India; *Corresponding author
Vikram Karande - E-mail: drvikramkarande@gmail.com; hoddental@aiimsrajkot.edu.in
Mohd Zeeshan Ahmad - E-mail: askzeeshan9@gmail.com
Vishal Kulkarni - E-mail: vishalkulkarni2aug@rediffmail.com
Priyanka Chandra - E-mail: priyanka_chandra123@yahoo.co.in
Sowjanya Gunukula - E-mail: drgunukula.dds@gmail.com
Mousami Kundu - E-mail: drmousamimds@gmail.com
Ritik Kashwani - E-mail: docritikkashwani@yahoo.com
Article Type
Research Article
Date
Received April 1, 2026; Revised April 30, 2026; Accepted April 30, 2026, Published April 30, 2026
Abstract
Early detection of dental caries is critical for implementing effective preventive measures, yet traditional radiographic interpretation often lacks consistency due to observer variability. Therefore, it is of interest to compare the diagnostic performance of AI-assisted and conventional radiographic interpretations for detecting early carious lesions. Hence, a total of 100 subjects were randomly assigned to either the AI-assisted or conventional interpretation group, with diagnostic accuracy assessed using sensitivity, specificity and overall accuracy. Thus, we show that AI-assisted interpretation outperformed conventional methods, with higher sensitivity (84%) and diagnostic accuracy (82%). This advancement highlights AI's potential to significantly improve early caries detection in dental practice.
Keywords
Artificial intelligence (AI), dental caries, diagnostic accuracy, early detection, radiographic interpretation
Citation
Karande et al. Bioinformation 22(4): 2278-2282 (2026)
Edited by
P Kangueane
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.
