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Automatic Detection of Cracks in Cracked Tooth Based on Binary Classification Convolutional Neural Networks.

Juncheng Guo¹, Yuyan Wu¹, Lizhi Chen¹

¹School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.

Applied Bionics and Biomechanics

|October 17, 2022

Summary

This study introduces a deep convolutional neural network (CNN) for detecting cracked teeth from images. The AI model achieved 90.39% accuracy, offering dentists a smarter diagnostic tool.

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Area of Science:

Dentistry
Artificial Intelligence
Medical Imaging

Background:

Cracked tooth syndrome presents diagnostic challenges due to subjective clinical assessments and limitations of traditional methods like radiography.
Subtle cracks are often missed, impacting treatment efficacy.
Deep convolutional neural networks (CNNs) show promise for image-based crack detection.

Purpose of the Study:

To develop and validate an image-based deep CNN method for detecting cracked teeth.
To improve diagnostic accuracy and efficiency in identifying dental cracks.

Main Methods:

A ResNet50-based CNN model was designed and trained on a dataset of 20,000 cracked tooth images.
A sliding window algorithm was integrated for comprehensive image analysis.
The optimal learning rate was determined to be 10^-5.

Main Results:

The CNN model achieved an average accuracy of 90.39% on 100 test images.
The method demonstrated effectiveness in detecting cracks across various image conditions, including staining and overexposure.
The system is applicable to both optical photographs and other medical imaging modalities.

Conclusions:

The proposed deep CNN method offers an intelligent and accurate solution for diagnosing cracked teeth.
This AI-driven approach can assist clinicians by improving the detection of subtle dental fractures.
The technology has potential for automated diagnosis across diverse dental imaging formats.