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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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Deep learning methods for fully automated dental age estimation on orthopantomograms.

Yuchao Shi¹, Zelin Ye¹, Jixiang Guo²

¹Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 3rd Section South Renmin Road 14#, Chengdu, 610041, China.

Clinical Oral Investigations

|March 6, 2024

Summary

This study introduces an automated dental age estimation method using a three-step framework for children aged 3-15. The system accurately determines tooth development and estimates dental age, improving diagnostic capabilities.

Keywords:

Artificial intelligence Deep learning Dental age Orthopantomogram Radiography

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Area of Science:

Dentistry
Artificial Intelligence
Medical Imaging

Background:

Accurate dental age estimation is crucial for pediatric health assessment and forensic science.
Existing methods for dental age assessment can be time-consuming and subjective.
A comprehensive automated approach for all permanent teeth developmental stages is needed.

Approach:

Developed a three-step automated framework for dental age estimation in children aged 3-15.
Utilized YOLOv3 for tooth localization and numbering on digital orthopantomograms.
Introduced SOS-Net for accurate tooth development staging based on a modified Demirjian method, followed by meta-analysis for dental age assessment.

Key Points:

YOLOv3 achieved 97.50% mean average precision for tooth determination.
SOS-Net demonstrated 82.97% average accuracy for tooth development staging.
The automated system achieved a Mean Absolute Error (MAE) of 0.72 years for dental age assessment.

Conclusions:

The proposed automated framework offers a fast, standardized, and accurate method for dental age estimation.
This system can be applied to diverse populations and aids in identifying abnormal tooth development.
Enhances the effectiveness and comprehensiveness of dental diagnoses from pediatric orthopantomograms.