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Prog Retin Eye Res. 2019 Apr 29. pii: S1350-9462(18)30090-9. doi: 10.1016/j.preteyeres.2019.04.003. [Epub ahead of print]

Deep learning in ophthalmology: The technical and clinical considerations.

Author information

1
Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore. Electronic address: daniel.ting.s.w@singhealth.com.sg.
2
Google AI Healthcare, California, USA.
3
Moorfields Eye Hospital, London, UK.
4
Wilmer Eye Institute, Johns Hopkins University School of Medicine, USA; Applied Physics Laboratory, Johns Hopkins University, USA; (f)Malone Center for Engineering in Healthcare, Johns Hopkins University, USA.
5
Departments of Ophthalmology & Medical Informatics and Clinical Epidemiology, Casey Eye Institute, Oregon Health and Science University, USA.
6
Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore; Department of Ophthalmology, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Department of Clinical Pharmacology, Medical University of Vienna, Austria; Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria.
7
Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
8
Wilmer Eye Institute, Johns Hopkins University School of Medicine, USA.
9
Department of Ophthalmology and Visual Sciences, University of Iowa Health Care, USA.
10
Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore.

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.

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