Deep learning in glaucoma with optical coherence tomography: a review
Publication in refereed journal


摘要Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, applying DL for glaucoma assessment with optical coherence tomography (OCT), including OCT traditional reports, two-dimensional (2D) B-scans, and three-dimensional (3D) volumetric scans, has increasingly raised research interests. Studies have demonstrated that using DL for interpreting OCT is efficient, accurate, and with good performance for discriminating glaucomatous eyes from normal eyes, suggesting that incorporation of DL technology in OCT for glaucoma assessment could potentially address some gaps in the current practice and clinical workflow. However, further research is crucial in tackling some existing challenges, such as annotation standardization (i.e., setting a standard for ground truth labelling among different studies), development of DL-powered IT infrastructure for real-world implementation, prospective validation in unseen datasets for further evaluation of generalizability, cost-effectiveness analysis after integration of DL, the AI “black box” explanation problem. This review summarizes recent studies on the application of DL on OCT for glaucoma assessment, identifies the potential clinical impact arising from the development and deployment of the DL models, and discusses future research directions.
著者Ran A.R., Tham C.C., Chan P.P., Cheng C.Y., Tham Y.C., Rim T.H., Cheung C.Y.
詳細描述The Erratum to this article has been published in Eye 2021 35:357 - "In the original Article, Dr. Poemen P. Chan’s name was originally misstated as “Poemen C. Chan”. This has been corrected in the PDF, HTML and XML versions of this Article."
出版社Springer Nature
頁次188 - 201

上次更新時間 2021-17-11 於 23:32