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Classification of periapical dental X-ray using the YOLOv8 deep learning model.

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This study enhances dental X-rays using Enhanced Super-Resolution GAN (ESRGAN) and detects six dental conditions with YOLOv8. The pipeline improves image clarity and aids automated dental assessment, though some conditions remain challenging.

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Dental X‑rayObject DetectionYOLOv8, deep learning model, Periapical, object classification, ESRGAN

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Image Analysis
  • Computer Vision

Background:

  • Dental radiographs are crucial for diagnosis but often suffer from noise, low resolution, and poor contrast.
  • These image quality issues significantly impact the accuracy of dental anomaly detection and diagnosis.

Purpose of the Study:

  • To develop and evaluate a two-stage pipeline for enhancing dental radiographs and detecting multiple dental conditions.
  • To combine Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) for image enhancement with YOLOv8 for object detection and classification of dental anomalies.

Main Methods:

  • Utilized ESRGAN with adaptive dual perceptual loss to improve image detail and resolution of dental radiographs.
  • Developed a customized YOLOv8 model trained to detect and classify six dental conditions: Caries, Crown, Root Canal Treated (RCT), Restoration, Normal, and Badly Decayed teeth.
  • Evaluated the ESRGAN-enhanced images using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the YOLOv8 model using mean Average Precision (mAP).

Main Results:

  • ESRGAN enhancement achieved a PSNR of 28.7 dB and SSIM of 0.91, demonstrating high visual fidelity.
  • The YOLOv8 model achieved an overall mAP@0.5 of 56.9% and mAP@0.5:0.95 of 41.6% on 100 test images.
  • High sensitivity (0.942) and specificity (0.919) were achieved for Crown detection, while Caries and Badly Decayed teeth detection showed lower sensitivity.

Conclusions:

  • The proposed ESRGAN and YOLOv8 pipeline shows clinical potential for improving dental radiograph quality and supporting automated dental assessment.
  • The enhancement significantly improved visualization of subtle details, aiding diagnosis.
  • Further research focusing on class-specific augmentation and explainability tools is recommended to enhance clinical utility, particularly for challenging classes like Caries and Badly Decayed teeth.