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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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Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images.

Jia-Xuan Han¹, Shi-Hui Shen¹, Yi-Wen Wu¹

¹Department of General Dentistry, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai 200011, China.

Fa Yi Xue Za Zhi

|June 7, 2024

Summary

Machine learning methods using cone beam computed tomography (CBCT) accurately estimate dental age in children and adolescents. Pulp volume is a key indicator for age estimation, outperforming traditional stepwise regression.

Keywords:

adolescents age estimation children cone beam computed tomography (CBCT)forensic anthropology forensic dentistry machine learning

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Area of Science:

Dentistry
Radiology
Machine Learning

Background:

Accurate dental age estimation is crucial for forensic and clinical applications.
Cone beam computed tomography (CBCT) provides detailed 3D imaging of dental structures.
Previous methods for dental age estimation have limitations in accuracy and efficiency.

Purpose of the Study:

To estimate the age of adolescents and children using machine learning and stepwise regression based on tooth and pulp volumes from CBCT images.
To compare the accuracy of different age estimation models.
To analyze the relationship between dental parameters and age.

Main Methods:

Collected 498 CBCT images of Shanghai Han adolescents and children.
Measured pulp and tooth volumes of the left maxillary central incisor and cuspid.
Developed four age estimation models using K-nearest neighbor, ridge regression, decision tree, and stepwise regression.
Evaluated models using R², mean error, RMSE, MSE, and MAE.

Main Results:

K-nearest neighbor (R²=0.779) and ridge regression (R²=0.729) models showed superior performance compared to stepwise regression (R²=0.617).
The decision tree model exhibited lower accuracy (R²=0.494).
A negative correlation was observed between age and pulp volume, pulp-to-hard tissue volume ratio, and pulp-to-tooth volume ratio.

Conclusions:

Pulp volume and its proportion are significant predictors of dental age.
CBCT-based machine learning models offer more accurate age estimation than traditional methods.
This research provides a foundation for advanced deep learning approaches in dental age estimation.