Deep learning in ophthalmology: The technical and clinical considerations. (September 2019)

• Record Type: Journal Article
• Authors: Ting, Daniel S.W.; Peng, Lily; Varadarajan, Avinash V.; Keane, Pearse A.; Burlina, Philippe M.; Chiang, Michael F.; Schmetterer, Leopold; Pasquale, Louis R.; Bressler, Neil M.; Webster, Dale R.; Abramoff, Michael; Wong, Tien Y.
• Is Part Of: Progress in retinal and eye research. Volume 72(2019:Sep.)
• Publication Date: 2019-09
• Subjects: Retina -- Periodicals; Retina -- Research -- Methodology -- Periodicals; Eye -- Diseases -- Periodicals; Eye -- Periodicals; Eye Diseases -- Periodicals; Retina -- Periodicals; Rétine -- Périodiques; Rétine -- Recherche -- Méthodologie -- Périodiques; 617.7005
• DOI: 10.1016/j.preteyeres.2019.04.003
• Languages: English
• ISSNs: 1350-9462

Abstract

Abstract: The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally.