ORIGINAL RESEARCH article
Front. Dent. Med.
Sec. Pediatric Dentistry
Volume 6 - 2025 | doi: 10.3389/fdmed.2025.1618246
This article is part of the Research TopicNext-Generation Pediatric Dentistry: The Role of Artificial Intelligence in Diagnosis and Patient CareView all articles
Automatic Dental Age Estimation in Adolescents via Oral Panoramic Imaging
Provisionally accepted
- 1Shanxi Medical University, Taiyuan, China
- 2Shanxi Provincial People's Hospital, Taiyuan, Shanxi Province, China
- 3Shanxi University of Finance and Economics, Taiyuan, Shanxi Province, China
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In forensic dentistry, dental age estimation assists experts in determining the age of victims or suspects, which is vital for legal responsibility and sentencing. The traditional Demirjian method assesses the development of seven mandibular teeth in pediatric dentistry, but it is time-consuming and relies heavily on subjective judgment. This study constructed a largescale panoramic dental image dataset and applied various convolutional neural network (CNN) models for automated age estimation. Model performance was evaluated using loss curves, residual histograms, and normal PP plots. Age prediction models were built separately for the total, female, and male samples. The best models yielded mean absolute errors of 1.24, 1.28, and 1.15 years, respectively. These findings confirm the effectiveness of deep learning models in dental age estimation, particularly among northern Chinese adolescents.
Keywords: forensic dentistry1, age estimation 2, pediatric dentistry3, Convolutional Neural Network4, Demirjian method5, oral panoramic imaging6
Received: 25 Apr 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Li, Xiao, Nan, Chen, Zhao, Wang, Guo and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Cairong Gao, Shanxi Medical University, Taiyuan, China
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