Title
Comparison of AI-based radiographic interpretation versus endodontic specialists for identifying periapical lesions: An in vitro study
Authors
Snehal Gosavi1, Jasmine Marwaha2, Naif Omar Binmuhana3, Santhosh Kumar Caliaperoumal4*, Bassam Alkhalifah5 & Mohammed Mustafa6
Affiliation
1Department of Conservative Dentistry and Endodontics , Al badar dental college and hospital, Gulbarga, Karnataka 585102, India; 2Department of Conservative dentistry and Endodontics, National Dental College and Hospital, Mohali-140507, India; 3Department of Preventive Dental Science, Division of Pediatric Dentistry, Ibn Sina National College for Medical Studies, Jeddah, Saudi Arabia; 4Department of dentistry, Vinayaka Mission’s Medical College and hospital, Vinayaka Mission’s Research Foundation (Deemed to be University), Karaikal – 609 609, Puducherry, India; 5Department of Radiology, College of Medicine, Qassim University, Buraydah,Saudi Arabia; 6Department of Conservative Dental Sciences, College Of Dentistry, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; *Corresponding author
Snehal gosavi - E-mail: snehalgosavi0@gmail.com
Jasmine Marwaha - E-mail: drjasminemarwaha@gmail.com
Naif Omar Binmuhana - E-mail: nbinmuhana@ibnsina.edu.sa
Santhosh Kumar Caliaperoumal - E-mail: sanjosh80@gmail.com
Bassam Alkhalifah - E-mail: b.alkhalifah@qu.edu.sa
Mohammed Mustafa - E-mail: ma.mustafa@psau.edu.sa
Article Type
Research Article
Date
Received November 15, 2025; Revised December 15, 2025; Accepted December 15, 2025, Published December 15, 2025
Abstract
The detection of periapical lesion is a diagnostic problem that sometimes entails the use of experts who can correctly interpret the radiographs to be accurate. Therefore, it is of interest to compare an AI system that uses CNN with endodontic experts in the classification of periapical lesions on 500 digital periapical radiographs. The AI was sensitive, specific and accurate with 89.2, 91.5, and 90.2 respectively, which is close to the performance of specialists and is able to process images more than 20 times faster. There were no differences in diagnostic measures of AI and experts. Thus, we show that AI-aided radiographic analysis may be used as a safe, time-saving supplement to the endodontic practice to detect periapical lesions.
Keywords
Artificial intelligence, deep learning, convolutional neural network, periapical lesions, endodontics, diagnostic accuracy, digital radiography
Citation
Gosavi et al. Bioinformation 21(12): 4831-4836 (2025)
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.
