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Updated: Nov 13, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Imaging sebaceous gland using optical coherence tomography with deep learning assisted automatic identification.

Yuemei Luo1, Xianghong Wang1, Xiaojun Yu2

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.

Journal of Biophotonics
|March 12, 2021
PubMed
Summary
This summary is machine-generated.

High-resolution optical coherence tomography (OCT) combined with deep learning accurately images sebaceous glands. This technology enables precise measurement of gland size and sebocyte layer thickness for diagnosing skin conditions.

Keywords:
computer-aided diagnosisdeep learningoptical coherence tomographyoptical imagingsebaceous glands

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Area of Science:

  • Biomedical Optics
  • Dermatology
  • Artificial Intelligence in Medicine

Background:

  • Accurate imaging and morphometric analysis of sebaceous glands are crucial for diagnosing and treating various skin conditions.
  • Current imaging techniques may have limitations in resolving fine structures of sebaceous glands.

Purpose of the Study:

  • To investigate the feasibility of using high-resolution optical coherence tomography (OCT) for sebaceous gland imaging.
  • To develop a deep learning model for automated identification and analysis of sebaceous glands in OCT images.

Main Methods:

  • Utilized high-resolution OCT with a spatial resolution of 2.3 μm (axial) × 6.2 μm (lateral) to image skin structures.
  • Developed a deep learning classification model based on ResNet18 for automatic identification of sebaceous glands in OCT images.
  • Demonstrated the capability to measure morphometric parameters such as gland size, sebocyte layer thickness, and gland density.

Main Results:

  • OCT successfully differentiated sebaceous glands from other skin structures, resolving the sebocyte layer.
  • The deep learning model achieved a high classification accuracy of 97.9% for identifying OCT images with sebaceous glands.
  • Automated analysis allowed for quantitative measurement of key sebaceous gland morphometric parameters.

Conclusions:

  • High-resolution OCT is a feasible and effective tool for imaging sebaceous glands.
  • Deep learning-assisted automated analysis significantly enhances the efficiency and accuracy of sebaceous gland imaging interpretation.
  • This integrated approach holds promise for improved diagnosis and treatment monitoring of sebaceous gland-related dermatological conditions.