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

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.