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Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks.

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This study introduces a new AI method using convolutional neural networks (CNNs) to detect approximal dental caries in bitewing radiographs. The Inception model achieved 73.3% accuracy, showing promise for aiding dental diagnostics.

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artificial intelligencebitewing radiographycariesdental radiographydentistrydiagnosisneural networks

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Dental caries, particularly approximal caries, pose diagnostic challenges due to their location.
  • Bitewing radiography is crucial for identifying approximal caries, but interpretation errors can occur.
  • Computational methods offer potential solutions for improving caries diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel method combining image processing and CNNs for identifying and classifying approximal dental caries.
  • To assess the performance of Inception and ResNet architectures in detecting caries severity from bitewing radiographs.

Main Methods:

  • Acquired 112 bitewing radiographs and extracted individual tooth images.
  • Applied data augmentation and trained CNN classification models (Inception, ResNet) on expert-labeled images.
  • Evaluated models using learning rates of 0.1, 0.01, and 0.001 over 2000 iterations.

Main Results:

  • The Inception model with a 0.001 learning rate achieved the highest accuracy of 73.3% on the test set.
  • The proposed method demonstrated promising results in identifying approximal dental caries and their severity.

Conclusions:

  • The developed AI-powered method shows potential as a tool to assist dentists in evaluating bitewing radiographs for approximal caries.
  • This approach could aid in defining lesion severity and guiding appropriate treatment decisions.