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Segmentation-Based Multi-Class Detection and Radiographic Charting of Periodontal and Restorative Conditions on

Ali Batuhan Bayırlı1, Buse Kesgin2, Mehmetcan Uytun1

  • 1Department of Periodontology, Faculty of Dentistry, Mugla Sıtkı Kocman University, 48000 Mugla, Türkiye.

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Summary

This study developed an AI model using YOLOv8x-seg to detect eight dental conditions on bitewing radiographs, achieving high accuracy for periodontal issues like bone loss and moderate success for restorative problems.

Keywords:
alveolar bone lossartificial intelligencebitewingcomputer-aided diagnosisdeep learningdental cariesradiography

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Bitewing radiographs are crucial for assessing dental caries, restorations, and periodontal status.
  • Existing AI research on dental imaging often overlooks multi-class diagnostic charting on bitewing radiographs.

Purpose of the Study:

  • To develop and evaluate a deep learning model (YOLOv8x-seg) for simultaneous detection of eight periodontal and restorative parameters on bitewing radiographs.
  • To assess the diagnostic performance of the AI model across different dental conditions.

Main Methods:

  • A dataset of 1197 digital bitewing radiographs was annotated by experts, yielding 7860 labels for eight conditions.
  • The YOLOv8x-seg model was trained on 80% of the data, with validation and testing on 10% each, utilizing extensive data augmentation.
  • Performance was evaluated using precision, recall, F1-score, and confusion matrices.

Main Results:

  • The model achieved high accuracy for alveolar bone loss (F1: 0.88).
  • Moderate performance was observed for dental calculus (F1: 0.58) and caries (F1: 0.57).
  • Lower detection rates were noted for less frequent conditions like cervical marginal gap (F1: 0.23) and secondary caries (F1: 0.29).

Conclusions:

  • The YOLOv8x-seg model demonstrates high success in detecting periodontal conditions and moderate success for restorative parameters on bitewing radiographs.
  • This AI framework offers a feasible approach for simultaneous evaluation of multiple dental findings, though performance varies by condition frequency.