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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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A fully automated classification of third molar development stages using deep learning.

Omid Halimi Milani¹, Salih Furkan Atici¹, Veerasathpurush Allareddy²

¹Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, USA.

Scientific Reports

|June 6, 2024

Summary

This study developed an automated method to classify third molar development stages using orthopantomograms (OPGs). EfficientNet achieved 83.7% accuracy, offering a valuable tool for dental diagnostics and treatment planning.

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Area of Science:

Dental Radiology
Artificial Intelligence in Medicine
Biometrics

Background:

Accurate classification of tooth development stages from orthopantomograms (OPGs) is vital for dental diagnosis, treatment planning, age assessment, and forensic science.
Automated methods can improve efficiency and consistency in analyzing OPGs.

Purpose of the Study:

To develop and evaluate an automated machine learning model for classifying third molar development stages using OPG images.
To compare the performance of various deep neural network architectures for this classification task.

Main Methods:

A dataset of 6624 OPG images (Q3 and Q4 regions) was curated and preprocessed.
Region of interest extraction, pre-filtering, and data augmentation were applied.
Deep neural network models including EfficientNet, EfficientNetV2, MobileNet, ResNet18, and ShuffleNet were trained and evaluated.

Main Results:

EfficientNet demonstrated the highest classification accuracy at 83.7%.
Other evaluated architectures achieved accuracies between 71.57% and 82.03%.
Model performance varied, indicating the impact of architecture complexity and feature extraction.

Conclusions:

A novel machine learning model effectively estimates lower wisdom tooth development stages from OPGs.
The developed automated method shows promise for enhancing dental diagnostics and treatment planning.
Further research can explore optimizing models for improved accuracy and broader clinical application.