Bulletin of Stomatology and Maxillofacial Surgery
ISSN 1829-006X
2025; 404–407
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AI-POWERED CARIES DETECTION IN PRIMARY TEETH USING DEEP LEARNING ANALYSIS OF DIGITAL RADIOGRAPHS AND INTRAORAL IMAGES

Anandamoy Bagchi*, Sudipta Kar, Shabnam Zahir

,Kalinga Institute of Dental Sciences (KIDS), Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar-751024, Odisha, India [email protected] · Institute of Dental Sciences and Research, Kolkata, West Bengal , India · Guru Nanak institute of dental Sciences & Research, Kolkata, India

Received: 2026-04-08 · Published: 2025-11-19

© 2025 The Author(s) and the original publisher.

CC BY-NC 4.0 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Original title
AI-POWERED CARIES DETECTION IN PRIMARY TEETH USING DEEP LEARNING ANALYSIS OF DIGITAL RADIOGRAPHS AND INTRAORAL IMAGES
Authors
Anandamoy Bagchi, Sudipta Kar, Shabnam Zahir
Source journal
Bulletin of Stomatology and Maxillofacial Surgery
Published
2025-11-19
Licence
Creative Commons Attribution-NonCommercial 4.0 International
Original
https://doi.org/10.58240/1829006X-2025.21.10-404

Abstract

Background: Dental caries in primary teeth remains a significant global health challenge, with early detection critical for preventing complications. Traditional diagnostic methods rely on subjective visual-tactile examination and radiography, which exhibit limitations in sensitivity and reproducibility. Artificial intelligence (AI) offers promising solutions for objective caries detection. Objective: To develop and validate a deep learning (DL) model for detecting caries in primary teeth using digital radiographs and intraoral images, and compare its performance against expert dentists. Methods: A retrospective study included 600 digital radiographs (bitewing) and 600 intraoral images from 320 children (aged 3–8 years). A convolutional neural network (CNN) was trained on 70% of the data, validated on 15%, and tested on 15%. Ground truth was established by two pediatric dentists. Performance metrics (accuracy, sensitivity, specificity) were calculated and compared to three general dentists using McNemar’s test. Key Findings: The DL model achieved an accuracy of 94.3% ± 1.4 for radiographs and 92.7% ± 1.6 for intraoral images. Sensitivity and specificity were 93.5% and 95.1% for radiographs, and 91.8% and 93.6% for intraoral images, respectively. The model significantly outperformed general dentists (accuracy: 84.2% ± 3.1; p < 0.001) and matched pediatric dentists (accuracy: 93.8% ± 1.2; p = 0.21). Conclusion: The DL model demonstrates high diagnostic accuracy for caries detection in primary teeth, surpassing general dentists and offering a reliable adjunctive tool for clinical practice.
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