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Teeth

Teeth

The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...

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Automated Age and Sex Estimation From Dental Panoramic Radiographs.

Natnicha Sributsayakarn¹, Thanapong Intharah², Natthanich Hirunchavarod²

¹Department of Pediatric Dentistry, Faculty of Dentistry, Mahidol University, Bangkok, Thailand.

International Dental Journal

|October 17, 2025

Summary

This study introduces an AI deep learning model for estimating age and sex from dental radiographs in Thai youth. The AI model shows promise as a faster, more accurate alternative to traditional forensic methods.

Keywords:

Age estimation Artificial intelligence Deep learning Forensic odontology Panoramic radiograph Sex estimation

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Area of Science:

Forensic Odontology
Artificial Intelligence
Medical Imaging

Background:

Traditional age and sex estimation methods in forensic odontology are complex, time-consuming, and prone to human error.
This study addresses these limitations by proposing an AI-driven approach using deep learning for age and sex estimation from panoramic radiographs.

Purpose of the Study:

To develop and evaluate a deep learning model for simultaneous age and sex estimation from panoramic radiographs of Thai children and adolescents.
To compare the performance of the AI model across different age groups (7-14 and 15-23 years).

Main Methods:

A supervised multitask deep learning model based on the EfficientNetB0 architecture was developed.
The model was trained on 4627 panoramic radiographs from 2491 Thai individuals aged 7 to 23 years.
Transfer learning, fine-tuning, and age-stratified models were employed to optimize predictive accuracy.

Main Results:

The overall age estimation model (7-23 years) achieved a root mean square error (RMSE) of 1.67 and mean absolute error (MAE) of 1.15.
The age-stratified model performed better in younger individuals (7-14 years: RMSE 0.95, MAE 0.62) than older ones (15-23 years: RMSE 1.87, MAE 1.41).
The sex recognition model demonstrated high performance (AUC=0.94, accuracy=87.8%), with improved accuracy in older individuals (15-23 years: AUC=0.99, accuracy=94.7%).

Conclusions:

The AI-based age and sex identification model shows significant potential as a diagnostic tool in forensic odontology.
This AI approach offers a promising alternative to traditional methods for characterizing individuals, both living and deceased.
The model's performance varies by age group, highlighting the need for age-specific considerations in AI applications for forensic identification.