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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Early detection of dental caries in children is vital for preventing irreversible damage.
  • Pediatric panoramic radiograph interpretation is challenging due to anatomical overlap and developmental variations.
  • Reliable computer-aided diagnostic models require well-curated, representative datasets.

Purpose of the Study:

  • Introduce the Mixed Dentition Orthopantomogram Dataset, a new, publicly available dataset for pediatric caries research.
  • Evaluate the dataset's utility for artificial intelligence (AI) research by benchmarking classification and segmentation models.
  • Demonstrate the potential of deep learning for analyzing mixed dentition panoramic images.

Main Methods:

  • Developed and curated the Mixed Dentition Orthopantomogram Dataset, including images from children aged 3-12 years.
  • Labeled proximal and occlusal caries regions by dental specialists.
  • Benchmarked the dataset using a patch-based classifier and U-Net/Attention U-Net segmentation models with various loss functions.

Main Results:

  • A patch-based classifier achieved an average AUC of 0.89 and Recall of 0.85 for distinguishing healthy and carious regions.
  • Attention U-Net with Focal loss yielded the best segmentation performance with a Dice score of 0.94.
  • The developed dataset supports AI research in pediatric dental caries analysis.

Conclusions:

  • The Mixed Dentition Orthopantomogram Dataset is a valuable resource for advancing AI in pediatric dentistry.
  • Deep learning models demonstrate significant potential for accurate caries detection in mixed dentition panoramic imaging.
  • This work facilitates improved diagnostic tools for early childhood caries detection.