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Automated Detection, Localization, and Severity Assessment of Proximal Dental Caries from Bitewing Radiographs Using

Mashail Alsolamy1, Farrukh Nadeem1, Amr Ahmed Azhari2

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22233, Saudi Arabia.

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Summary

A new deep learning system accurately detects and grades proximal dental caries from bitewing radiographs, aiding dentists in diagnosis and treatment planning for this common oral infection.

Keywords:
YOLO networkartificial intelligencebitewing radiographsdiagnosisinstance segmentationproximal caries

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Imaging Analysis
  • Oral Health Informatics

Background:

  • Dental caries is a prevalent chronic infection impacting a significant portion of the population.
  • Proximal caries are challenging to detect early due to their hidden location, often requiring radiographic assessment.
  • Bitewing radiographs (BRs) are crucial for detecting proximal caries, but interpretation errors can lead to misdiagnosis.

Purpose of the Study:

  • To develop and evaluate a deep learning system for automated detection and severity classification of proximal dental caries from BRs.
  • To align caries severity assessment with the International Caries Classification and Management System (ICCMS) guidelines.
  • To enhance diagnostic accuracy and efficiency in identifying proximal caries.

Main Methods:

  • A deep learning system utilizing a pre-trained YOLOv11 instance segmentation model was developed.
  • The system performs three core tasks: caries detection, tooth numbering, and caries localization (tooth and surface).
  • A dataset of 1354 BRs, annotated by a restorative dentistry consultant, was used for training (80%), validation (10%), and testing (10%).

Main Results:

  • The system achieved high performance metrics: precision 0.844, recall 0.864, F1-score 0.851, and mean Average Precision (mAP) 0.888 for caries segmentation and severity classification.
  • 100% accuracy was attained in identifying affected teeth and surfaces for teeth fully or three-quarters visible in BRs.
  • The system demonstrated superior sensitivity and accuracy compared to manual dentist evaluations.

Conclusions:

  • The proposed deep learning system shows significant promise for assisting dentists in the interpretation of BRs.
  • It can effectively aid in the detection and severity assessment of proximal dental caries.
  • The system has the potential to improve treatment planning and patient outcomes for caries management.