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Automated Classification of Dental Caries in Bitewing Radiographs Using Machine Learning and the ICCMS Framework.

Mehdi Salehizeinabadi1, Saghar Neghab2, Nazila Ameli1

  • 1Department of Dentistry, Mike Petryk School of Dentistry, University of Alberta, Edmonton, Alberta, Canada.

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Summary

The YOLOv11 artificial intelligence model effectively detects advanced dental caries in radiographs but shows limitations with early-stage lesions. Further AI development is needed to improve early detection and patient outcomes.

Keywords:
YOLO networkartificial intelligencebitewing radiographsdental cariesmachine learning

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Dental caries is a significant public health concern requiring early detection for effective management.
  • Traditional methods for diagnosing dental caries, such as visual examination and radiographs, can be subjective and vary in interpretation.
  • Artificial intelligence (AI), particularly deep learning (DL), offers potential for enhancing diagnostic accuracy in dentistry.

Purpose of the Study:

  • To evaluate the performance of the YOLOv11 deep learning model for detecting and segmenting dental caries in bitewing radiographs.
  • To assess the model's accuracy against the standardized International Caries Classification and Management System (ICCMS) framework.
  • To identify the model's strengths and weaknesses in diagnosing different stages of dental caries.

Main Methods:

  • A dataset of 730 bitewing radiographs with 1115 annotated carious lesions was utilized.
  • Annotations were performed by experienced dentists, with inter- and intra-rater reliability assessed using Intersection over Union (IoU) and Dice similarity coefficient (DSC).
  • The YOLOv11 model was trained for 50 epochs with data augmentation, and performance was evaluated using precision, recall, and mean average precision (mAP50).

Main Results:

  • High inter-rater agreement (IoU: 0.82, DSC: 0.85) and intra-rater agreement (IoU: 0.84, DSC: 0.87) were observed in lesion annotations.
  • The YOLOv11 model achieved high mAP50 scores for advanced caries (RB4+RC5: 0.74, RC6: 0.80).
  • Performance was moderate for early-stage lesions (RA1+RA2: 0.61, RA3: 0.52), indicating a need for improvement.

Conclusions:

  • The YOLOv11 model demonstrates high efficacy in detecting advanced dental caries from bitewing radiographs.
  • The model's performance is less robust for early-stage caries detection, suggesting areas for future AI model refinement.
  • Integrating AI into dental radiographic analysis holds promise for improving diagnostic accuracy, facilitating early interventions, and enhancing patient outcomes.