Prediction of Visual Impairment in Epiretinal Membrane and Feature Analysis: A Deep Learning Approach Using Optical Coherence Tomography

Yun Hsia, Yu Yi Lin, Bo Sin Wang, Chung Yen Su, Ying Hui Lai, Yi Ting Hsieh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: The aim was to develop a deep learning model for predicting the extent of visual impairment in epiretinal membrane (ERM) using optical coherence tomography (OCT) images, and to analyze the associated features. Methods: Six hundred macular OCT images from eyes with ERM and no visually significant media opacity or other retinal diseases were obtained. Those with best-corrected visual acuity ≤20/50 were classified as "profound visual impairment,"while those with best-corrected visual acuity >20/50 were classified as "less visual impairment."Ninety percent of images were used as the training data set and 10% were used for testing. Two convolutional neural network models (ResNet-50 and ResNet-18) were adopted for training. The t-distributed stochastic neighbor-embedding approach was used to compare their performances. The Grad-CAM technique was used in the heat map generative phase for feature analysis. Results: During the model development, the training accuracy was 100% in both convolutional neural network models, while the testing accuracy was 70% and 80% for ResNet-18 and ResNet-50, respectively. The t-distributed stochastic neighbor-embedding approach found that the deeper structure (ResNet-50) had better discrimination on OCT characteristics for visual impairment than the shallower structure (ResNet-18). The heat maps indicated that the key features for visual impairment were located mostly in the inner retinal layers of the fovea and parafoveal regions. Conclusions: Deep learning algorithms could assess the extent of visual impairment from OCT images in patients with ERM. Changes in inner retinal layers were found to have a greater impact on visual acuity than the outer retinal changes.

Original languageEnglish
Pages (from-to)21-28
Number of pages8
JournalAsia-Pacific Journal of Ophthalmology
Volume12
Issue number1
DOIs
Publication statusPublished - 2023 Jan 11

Keywords

  • artificial intelligence
  • deep learning
  • epiretinal membrane
  • optical coherence tomography

ASJC Scopus subject areas

  • Ophthalmology

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