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Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs.

Toan Huy Bui1, Kazuhiko Hamamoto2, May Phu Paing3

  • 1Course of Science and Technology, Graduate School of Science and Technology, Tokai University, Tokyo 108-8619, Japan.

Entropy (Basel, Switzerland)
|July 8, 2023
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Summary

This study introduces an automated method for diagnosing dental caries using panoramic radiographs. The AI system accurately identifies caries, improving oral hygiene and reducing diagnostic errors.

Keywords:
caries screeningdeep learningdental radiographsensemble

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Dental caries prevention is crucial for maintaining oral hygiene.
  • Current diagnostic methods can be labor-intensive and prone to human error.
  • There is a need for automated, reliable dental diagnostic tools.

Purpose of the Study:

  • To develop and evaluate a fully automated method for caries diagnosis from panoramic radiographs.
  • To segment individual teeth from radiographs and extract diagnostic features.
  • To improve the accuracy and reliability of dental caries detection.

Main Methods:

  • Automated segmentation of tooth regions from panoramic radiographs.
  • Feature extraction using pre-trained deep learning networks (VGG, Resnet, Xception).
  • Classification using ensemble methods (Random Forest, KNN, SVM) with majority voting for final diagnosis.

Main Results:

  • The proposed automated method achieved high diagnostic accuracy.
  • Achieved an accuracy of 93.58%, sensitivity of 93.91%, and specificity of 93.33%.
  • Outperformed existing methods in terms of reliability and diagnostic efficiency.

Conclusions:

  • The developed automated system shows significant promise for widespread implementation in dental facilities.
  • This method can facilitate dental diagnosis, reduce manual labor, and minimize diagnostic errors.
  • The approach offers a reliable and efficient tool for caries prevention and management.