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

• Published in: Scientific reports Vol. 10; no. 1; p. 8424
• Main Authors: Lo, Ying-Chih, Lin, Keng-Hung, Bair, Henry, Sheu, Wayne Huey-Herng, Chang, Chi-Sen, Shen, Ying-Cheng, Hung, Che-Lun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.05.2020; Nature Publishing Group
• Subjects: 639/166; 692/308; 692/699; 692/700; Age; Algorithms; Cross-Sectional Studies; Datasets; Deep Learning; Diabetes mellitus; Diabetic retinopathy; Diabetic Retinopathy - diagnosis; Diabetic Retinopathy - diagnostic imaging; Diagnosis, Computer-Assisted - methods; Epiretinal Membrane - diagnostic imaging; Eye; Humanities and Social Sciences; Humans; Macular degeneration; Macular Degeneration - diagnosis; Macular Degeneration - diagnostic imaging; Medical personnel; multidisciplinary; Ophthalmologists; Retina; Retina - pathology; Retinopathy; Science; Science (multidisciplinary); Tomography; Tomography, Optical Coherence - methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-65405-2

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.