Nishaant Bhambra1, Fares Antaki2,3, Farida El Malt1, AnQi Xu4, Renaud Duval5,6. 1. Faculty of Medicine, McGill University, Montréal, Québec, Canada. 2. Department of Ophthalmology, Université de Montréal, Montréal, Québec, Canada. 3. Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS de L'Est-de-L'Île-de-Montréal, 5415 Assumption Blvd, Montréal, Québec, H1T 2M4, Canada. 4. Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada. 5. Department of Ophthalmology, Université de Montréal, Montréal, Québec, Canada. renaud.duval@umontreal.ca. 6. Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS de L'Est-de-L'Île-de-Montréal, 5415 Assumption Blvd, Montréal, Québec, H1T 2M4, Canada. renaud.duval@umontreal.ca.
Abstract
PURPOSE: 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. METHODS: 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 The exclusion criteria were as follows: (1) articles published in any language other than English, (2) articles not peer-reviewed (usually preprints), (3) no full-text availability, (4) articles using machine learning algorithms other than deep learning.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. RESULTS: 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. CONCLUSION: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Authors: Netan Choudhry; Jay S Duker; K Bailey Freund; Szilard Kiss; Giuseppe Querques; Richard Rosen; David Sarraf; Eric H Souied; Paulo E Stanga; Giovanni Staurenghi; SriniVas R Sadda Journal: Ophthalmol Retina Date: 2019-05-13
Authors: Nicola B Quinn; Augusto Azuara-Blanco; Katie Graham; Ruth E Hogg; Ian S Young; Frank Kee Journal: Ophthalmic Epidemiol Date: 2017-09-18 Impact factor: 1.648
Authors: Matthew M Wessel; Grant D Aaker; George Parlitsis; Minhee Cho; Donald J D'Amico; Szilárd Kiss Journal: Retina Date: 2012-04 Impact factor: 4.256
Authors: Livia Faes; Siegfried K Wagner; Dun Jack Fu; Xiaoxuan Liu; Edward Korot; Joseph R Ledsam; Trevor Back; Reena Chopra; Nikolas Pontikos; Christoph Kern; Gabriella Moraes; Martin K Schmid; Dawn Sim; Konstantinos Balaskas; Lucas M Bachmann; Alastair K Denniston; Pearse A Keane Journal: Lancet Digit Health Date: 2019-09-05