International dental journal · 2025
Automated Age and Sex Estimation From Dental Panoramic Radiographs.
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Sributsayakarn Natnicha, Intharah Thanapong, Hirunchavarod Natthanich, Pornprasertsuk-Damrongsri Suchaya, Jirarattanasopha Varangkanar
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.