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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Optical coherence tomography (OCT) offers high-resolution subsurface imaging but requires complex data analysis.
  • Adoption of OCT as a biopsy guidance tool in the oral cavity is limited by data processing challenges.

Purpose of the Study:

  • To develop automated deep learning software for simplified analysis of OCT data from the oral cavity.
  • To reduce barriers to using OCT as a biopsy guidance device in dentistry and oral medicine.

Main Methods:

  • A wide-field endoscopic OCT system was used for in-clinic imaging.
  • A dataset of 294 OCT images from 60 patients was annotated.
  • Four convolutional neural networks (modified U-Net) were trained for field of view detection, artifact detection, surface segmentation, and epithelial-stromal boundary identification.

Main Results:

  • High accuracy was achieved: Area Under the Curve (AUC) of 1.00 for image detection and 0.94 for artifact detection.
  • Segmentation performance was strong: Dice similarity scores of 0.98 for surface segmentation and 0.83 for epithelial-stromal boundary segmentation.
  • Deep learning models successfully identified and segmented the epithelial surface and epithelial-stromal boundary in oral mucosa OCT images.

Conclusions:

  • Deep learning-based automated software significantly simplifies OCT data analysis for oral subsurface morphology.
  • The developed tools facilitate easier visualization of tissue variations through en face maps, supporting OCT's role in biopsy guidance.