Automated Age and Sex Estimation From Dental Panoramic Radiographs
Issued Date
2025-12-01
Resource Type
ISSN
00206539
eISSN
1875595X
Scopus ID
2-s2.0-105020468196
Pubmed ID
41106218
Journal Title
International Dental Journal
Volume
75
Issue
6
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Dental Journal Vol.75 No.6 (2025)
Suggested Citation
Sributsayakarn N., Intharah T., Hirunchavarod N., Pornprasertsuk-Damrongsri S., Jirarattanasopha V. Automated Age and Sex Estimation From Dental Panoramic Radiographs. International Dental Journal Vol.75 No.6 (2025). doi:10.1016/j.identj.2025.103967 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112952
Title
Automated Age and Sex Estimation From Dental Panoramic Radiographs
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Age and sex estimation, which is crucial in forensic odontology, traditionally relies on complex, time-consuming methods prone to human error. This study proposes an AI-driven approach using deep learning to estimate age and sex from panoramic radiographs of Thai children and adolescents. This study analyzed 4627 images from 2491 panoramic radiographs of Thai individuals aged 7 to 23 years. A supervised multitask model, built upon the EfficientNetB0 architecture, was developed to simultaneously estimate age and classify sex. The model was trained using a 2-phase process of transfer learning and fine-tuning. Following the development of an initial baseline model for the entire 7 to 23-year cohort, 2 primary age-stratified models (7-14 and 15-23 years) were subsequently developed to enhance predictive accuracy. All models were validated against the subjects' chronological age and biological sex. The age estimation model for individuals aged 7 to 23 years yielded a root mean square error (RMSE) of 1.67 and mean absolute error (MAE) of 1.15, with 71.0% accuracy in predicting dental-chronological age differences within 1 year. Age-stratified analysis revealed that the model showed superior performance in the younger cohort (7-14 years), with RMSE of 0.95, MAE of 0.62, and accuracy of 90.3%. Performance declined substantially in the older age group (15-23 years), where RMSE, MAE, and accuracy values were 1.87, 1.41, and 63.8%, respectively. The sex recognition model achieved good overall performance for individuals aged 7 to 23 years (area under curve [AUC] = 0.94, accuracy = 87.8%, sensitivity = 89%, specificity = 87%). In contrast to age estimation, sex recognition performance improved notably in the older cohort (15-23 years): AUC of 0.99, 94.7% accuracy, 92% sensitivity, and 98% specificity. This novel AI-based age and sex identification model exhibited good performance metrics, suggesting the potential to serve as an alternative to traditional methods as a diagnostic tool for characterizing both living individuals, as well as deceased bodies.