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Related Experiment Video

Updated: Nov 16, 2025

Doppler Optical Coherence Tomography of Retinal Circulation
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Classification of pachychoroid on optical coherence tomography using deep learning.

Nam Yeo Kang1, Ho Ra1, Kook Lee2

  • 1Department of Ophthalmology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Gyeonggi-do, Republic of Korea.

Graefe'S Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie
|February 22, 2021
PubMed
Summary

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Deep learning models accurately classify pachychoroid and non-pachychoroid eye conditions using optical coherence tomography (OCT) B-scans. Advanced convolutional neural networks (CNNs) show superior performance for this diagnostic task.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Pachychoroid is an eye condition characterized by specific changes in choroidal vessels visible on OCT B-scans.
  • Accurate classification of pachychoroid is crucial for understanding and managing related macular diseases.

Purpose of the Study:

  • To evaluate the feasibility of using deep learning (DL) models for classifying pachychoroid versus non-pachychoroid eyes.
  • To compare the performance of different DL architectures in this classification task.

Main Methods:

  • A dataset of 1898 OCT B-scan images from eyes with macular diseases was utilized.
  • Images were meticulously labeled as pachychoroid or non-pachychoroid by two retina specialists.
  • Deep learning models, including pretrained convolutional neural networks (CNNs), were trained and validated.
Keywords:
AMDArtificial intelligenceCSCConvolutional neural networkOptical coherence tomographyPCVPachychoroid

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Main Results:

  • While shallow CNNs (AlexNet, VGG-16) showed lower accuracy (57.52%), deeper models like ResNet50, Inception-v3, Inception-ResNet-v2, and Xception achieved high validation accuracies (up to 96.31%).
  • On an independent test set, these advanced models demonstrated accuracies ranging from 78.00% to 92.00% and F1 scores from 0.718 to 0.920.
  • ResNet50, Inception-v3, Inception-ResNet-v2, and Xception exhibited strong diagnostic performance.

Conclusions:

  • Deep learning models, particularly deeper CNN architectures, are effective for classifying pachychoroid and non-pachychoroid eyes from OCT B-scans.
  • The findings support the use of advanced DL models for automated diagnosis in ophthalmology.