Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning

• Published in: Ophthalmic research Vol. 66; no. 1; pp. 238 - 246
• Main Authors: Tang, Yong, Gao, Xiaorong, Wang, Weijia, Dan, Yujiao, Zhou, Linjing, Su, Song, Wu, Jiali, Lv, Hongbin, He, Yue
• Format: Journal Article
• Language: English
• Published: Basel, Switzerland S. Karger AG 01.01.2023
• Subjects: Algorithms; Artificial intelligence; Automation; Comparative analysis; Data mining; Deep Learning; Epiretinal Membrane - diagnosis; Humans; Mechanization; Medical imaging equipment; Research Article; Retina; Retrospective Studies; Tomography, Optical Coherence - methods; China; Deep learning; Optical coherence tomography; Macular epiretinal membranes; Artificial intelligence
• ISSN: 0030-3747; 1423-0259; 1423-0259
• DOI: 10.1159/000525929

Abstract Introduction: Development and validation of a deep learning algorithm to automatically identify and locate epiretinal memberane (ERM) regions in OCT images. Methods: OCT images of 468 eyes were retrospectively collected from a total of 404 ERM patients. One expert manually annotated the ERM regions for all images. A total of 422 images (90%) and the remainig 46 images (10%) were used as the training dataset and validation dataset for deep learning algorithm training and validation, respectively. One senior and one junior clinician read the images. The diagnostic results were compared. Results: The algorithm accurately segmented and located the ERM regions in OCT images. The image-level accuracy was 95.65%, and the ERM region-level accuracy was 90.14%, respectively. In comparison experiments, the accuracies of the junior clinician improved from 85.00% to 61.29% without the assistance of the algorithm to 100.00% and 90.32% with the assistance of the algorithm. The corresponding results of the senior clinician were 96.15%, 95.00% without the assistance of the algorithm, and 96.15%, 97.50% with the assistance of the algorithm. Conclusions: The developed deep learning algorithm can accurately segment ERM regions in OCT images. This deep learning approach may help clinicians in clinical diagnosis with better accuracy and efficiency.