Artificial intelligence and deep learning in ophthalmology

British Journal of Ophthalmology

Volumn 103, Issue 2, 2019, Pages 167-175

  Ting, Daniel Shu Wei ^a   Pasquale, Louis R ^b   Peng, Lily ^c   Campbell, John Peter ^d   Lee, Aaron Y ^e   Raman, Rajiv ^f   Tan, Gavin Siew Wei ^a   Schmetterer, Leopold ^a,g,h,i   Keane, Pearse A ^j   Wong, Tien Yin ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b MOUNT SINAI HOSPITAL   (United States)

^c GOOGLE INC   (United States)

^d OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^e UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

^f SANKARA NETHRALAYA   (India)

^g NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^h MEDICAL UNIVERSITY OF VIENNA   (Austria)

ⁱ Christian Doppler Laboratory for Innovative Optical Imaging and its Translation to Medicine   (Austria)

^j MOORFIELDS EYE HOSPITAL   (United Kingdom)

more..

Author keywords

glaucoma; imaging; public health; retina; telemedicine

Indexed keywords

AGE RELATED MACULAR DEGENERATION; ARTIFICIAL INTELLIGENCE; CLINICAL FEATURE; DEEP LEARNING; DIABETIC RETINOPATHY; DISEASE ASSOCIATION; GLAUCOMA; HUMAN; INCIDENCE; OPHTHALMOLOGY; POPULATION RISK; PREVALENCE; PRIORITY JOURNAL; RETROLENTAL FIBROPLASIA; REVIEW; RISK FACTOR; SUBRETINAL NEOVASCULARIZATION; ANIMAL; EYE DISEASE; PROCEDURES;

ANIMALS; ARTIFICIAL INTELLIGENCE; DEEP LEARNING; EYE DISEASES; HUMANS; OPHTHALMOLOGY;

EID: 85055474664     PISSN: 00071161     EISSN: 14682079     Source Type: Journal    
DOI: 10.1136/bjophthalmol-2018-313173     Document Type: Review

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