Epiretinal Membrane Detection at the Ophthalmologist Level using Deep Learning of Optical Coherence Tomography

Ying-Chih Lo,Henry Bair,Keng-Hung Lin,Chi-Sen Chang,Che-Lun Hung,Ying-Cheng Shen,Wayne Huey-Herng Sheu

doi:10.1038/s41598-020-65405-2

Abstract

Purpose: Previous deep learning studies on optical coherence tomography (OCT) mainly focused on diabetic retinopathy and age-related macular degeneration. We proposed a deep learning model that can identify epiretinal membrane (ERM) in OCT with ophthalmologist-level performance. Design: Cross-sectional study. Participants: A total of 3,618 central fovea cross section OCT images from 1,475 eyes of 964 patients. Methods: We retrospectively collected 7,652 OCT images from 1,197 patients. From these images, 2,171 were normal and 1,447 were ERM OCT. A total of 3,141 OCT images was used as training dataset and 477 images as testing dataset. DL algorithm was used to train the interpretation model. Diagnostic results by four board-certified non-retinal specialized ophthalmologists on the testing dataset were compared with those generated by the DL model. Main Outcome Measures: We calculated for the derived DL model the following characteristics: sensitivity, specificity, F1 score and area under curve (AUC) of the receiver operating characteristic (ROC) curve. These were calculated according to the gold standard results which were parallel diagnoses of the retinal specialist. Performance of the DL model was finally compared with that of non-retinal specialized ophthalmologists. Results: Regarding the diagnosis of ERM in OCT images, the trained DL model had the following characteristics in performance: sensitivity: 98.7%, specificity: 98.0%, and F1 score: 0.945. The accuracy on the training dataset was 99.7% (95% CI: 99.4 - 99.9%), and for the testing dataset, diagnostic accuracy was 98.1% (95% CI: 96.5 - 99.1%). AUC of the ROC curve was 0.999. The DL model slightly outperformed the average non-retinal specialized ophthalmologists. Conclusions: An ophthalmologist-level DL model was built here to accurately identify ERM in OCT images. The performance of the model was slightly better than the average non-retinal specialized ophthalmologists. The derived model may play a role to assist clinicians to promote the efficiency and safety of healthcare in the future.

Highlights

An epiretinal membrane (ERM), known as macular pucker or cellophane maculopathy, is a pathological fibrocellular tissue that forms on the inner surface of the retina
The optical coherence tomography (OCT) images were obtained with spectral-domain OCT (Spectralis; Heidelberg Engineering, Heidelberg, Germany) and the raw image data were stored in a centralized workstation
3,141 OCT images were used for model training and 20% (n = 628) of them were validation dataset

Summary

Introduction

An epiretinal membrane (ERM), known as macular pucker or cellophane maculopathy, is a pathological fibrocellular tissue that forms on the inner surface of the retina. OCT plays a vital role in visualizing ERMs, determining the appropriate timing and procedures for their management, as well as the prediction of postoperative outcomes[17]. Despite the diagnostic advantage of OCT on ocular diseases, interpretation of images is a time-consuming procedure for ophthalmologists. To accelerate the diagnostic process, several studies on ocular images were made to automate the interpretation workflow using various computer vision approaches[18,19]. Due to the rapid growth of data volume and computational capacity, DL approaches have made great advancements in many fields, such as computer vision, voice recognition and nature language processing. The surprising improvement over conventional approaches has positioned DL in the mainstream technique in implementing applications of the artificial intelligence

Methods

Results

Conclusion