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Predicting central choroidal thickness from colour fundus photographs using deep learning.

Yusuke Arai1, Hidenori Takahashi1, Takuya Takayama1

  • 1Department of Ophthalmology, Jichi Medical University, Shimotsuke, Tochigi, Japan.

Plos One
|March 29, 2024
PubMed
Summary

A new deep learning method accurately estimates central choroidal thickness from fundus images. This approach aids in detecting choroidal thickening and thinning, improving eye disease diagnosis.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate estimation of central choroidal thickness (CCT) is crucial for diagnosing various retinal diseases.
  • Current methods for CCT measurement can be invasive or require specialized equipment.
  • Colour fundus images are widely available, making them an attractive source for CCT estimation.

Purpose of the Study:

  • To develop and validate a deep learning (DL) algorithm for estimating CCT from colour fundus images.
  • To assess the algorithm's performance using independent datasets from multiple institutions.
  • To demonstrate the potential of DL in enhancing the diagnostic capabilities for choroidal abnormalities.

Main Methods:

  • A DL model was trained using 2,548 colour fundus images from Jichi Medical University Hospital.
  • The model was validated on a separate dataset of 393 images from three different institutions.
  • Exclusion criteria included specific pathologies like subretinal hemorrhage and macular edema; images with visible pigment epithelium were included.

Main Results:

  • The DL algorithm achieved a standard deviation of 73 μm during 10-fold cross-validation.
  • Re-training the algorithm with data from all institutions significantly reduced the standard deviation for external validation datasets.
  • This indicates improved generalizability and accuracy across different imaging sources.

Conclusions:

  • This study presents the first DL-based method for estimating CCT from colour fundus images.
  • The validated algorithm demonstrates robust performance and potential for clinical application.
  • The tool is expected to assist clinicians in assessing choroidal thickening and thinning, aiding in disease detection.