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Diagnosis of Interproximal Caries Lesions in Bitewing Radiographs Using a Deep Convolutional Neural Network-Based

Ángel García-Cañas1, Mónica Bonfanti-Gris1, Sergio Paraíso-Medina2

  • 1Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid, Madrid, Spain.

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Summary

This study found that a web-based artificial intelligence program demonstrates good diagnostic reliability for detecting interproximal dental caries in radiographs. Model 2 showed the best performance in distinguishing between healthy and decayed teeth.

Keywords:
Artificial intelligenceCaries detectionComputer-aided diagnosisConvolutional neural networks

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Radiology

Background:

  • Dental caries detection from radiographs is crucial for timely intervention.
  • Traditional methods can be subjective and time-consuming.
  • AI offers potential for objective and efficient analysis of dental images.

Purpose of the Study:

  • To evaluate the diagnostic reliability of a web-based AI program for detecting interproximal caries.
  • To compare AI performance against clinical validation using bitewing radiographs.
  • To identify the optimal AI model threshold for caries detection.

Main Methods:

  • 300 bitewing radiographs were analyzed by a trained operator and a convolutional neural network (CNN) AI program.
  • Ground truth was established through clinical-visual, radiographic, and instrumental assessments.
  • Four AI models were tested using different confidence thresholds (0-100%) for caries detection.

Main Results:

  • AI models achieved accuracy rates from 70.8% to 86.1%.
  • Specificity increased with higher confidence thresholds, reaching 98.5% for Model 4.
  • Model 2 (≥25% threshold) demonstrated the best overall balance for differentiating healthy from decayed teeth, with an accuracy of 82% and high sensitivity and specificity.

Conclusions:

  • Web-based AI software shows promising diagnostic reliability for detecting dental caries.
  • The AI program can aid clinicians in identifying interproximal caries.
  • Further research may refine AI algorithms for improved diagnostic accuracy in dentistry.