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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

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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

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 screening deep learning dental radiographs ensemble

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Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera

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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.