Bhambra Nishaant, Antaki Fares, Malt Farida El, Xu AnQi, Duval Renaud
Faculty of Medicine, McGill University, Montréal, Québec, Canada.
Department of Ophthalmology, Université de Montréal, Montréal, Québec, Canada.
Graefes Arch Clin Exp Ophthalmol. 2022 Dec;260(12):3737-3778. doi: 10.1007/s00417-022-05741-3. Epub 2022 Jul 20.
This article is a scoping review of published and peer-reviewed articles using deep-learning (DL) applied to ultra-widefield (UWF) imaging. This study provides an overview of the published uses of DL and UWF imaging for the detection of ophthalmic and systemic diseases, generative image synthesis, quality assessment of images, and segmentation and localization of ophthalmic image features.
A literature search was performed up to August 31st, 2021 using PubMed, Embase, Cochrane Library, and Google Scholar. The inclusion criteria were as follows: (1) deep learning, (2) ultra-widefield imaging. The exclusion criteria were as follows: (1) articles published in any language other than English, (2) articles not peer-reviewed (usually preprints), (3) no full-text availability, (4) articles using machine learning algorithms other than deep learning. No study design was excluded from consideration.
A total of 36 studies were included. Twenty-three studies discussed ophthalmic disease detection and classification, 5 discussed segmentation and localization of ultra-widefield images (UWFIs), 3 discussed generative image synthesis, 3 discussed ophthalmic image quality assessment, and 2 discussed detecting systemic diseases via UWF imaging.
The application of DL to UWF imaging has demonstrated significant effectiveness in the diagnosis and detection of ophthalmic diseases including diabetic retinopathy, retinal detachment, and glaucoma. DL has also been applied in the generation of synthetic ophthalmic images. This scoping review highlights and discusses the current uses of DL with UWF imaging, and the future of DL applications in this field.
本文是一篇对已发表且经过同行评审的、将深度学习（DL）应用于超广角（UWF）成像的文章的范围综述。本研究概述了DL和UWF成像在眼科和全身性疾病检测、生成性图像合成、图像质量评估以及眼科图像特征分割与定位方面的已发表应用。
截至2021年8月31日，使用PubMed、Embase、Cochrane图书馆和谷歌学术进行文献检索。纳入标准如下：（1）深度学习；（2）超广角成像。排除标准如下：（1）非英文发表的文章；（2）未经同行评审的文章（通常是预印本）；（3）无全文可获取；（4）使用除深度学习之外的机器学习算法的文章。不排除任何研究设计。
共纳入36项研究。23项研究讨论了眼科疾病的检测和分类，5项讨论了超广角图像（UWFIs）的分割和定位，3项讨论了生成性图像合成，3项讨论了眼科图像质量评估，2项讨论了通过UWF成像检测全身性疾病。
DL在UWF成像中的应用已在糖尿病视网膜病变、视网膜脱离和青光眼等眼科疾病的诊断和检测中显示出显著效果。DL还被应用于合成眼科图像的生成。本范围综述突出并讨论了DL与UWF成像的当前应用，以及该领域DL应用的未来发展。
