Deep learning in ophthalmology: The technical and clinical considerations

Author:

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 MichaelORCID,Wong Tien Y.

Funder

National Medical Research Council (NMRC) Health Service Research Grant and Large Collaborative Grant

Ministry of Health

Singapore National Health Innovation Center, Innovation to Develop Grant

SingHealth Foundation Research Grant

Tanoto Foundation

Johns Hopkins University School of Medicine

NMRC

MOH

Biomedical Research Council

MOH, Singapore

University of Iowa Hospital and Clinics

Singapore Eye Research Institute

National University Singapore School of Computing, Singapore

A*STAR Institute of High Performance Center, Singapore

Google AI Health, California

Publisher

Elsevier BV

Subject

Sensory Systems,Ophthalmology

Reference147 articles.

1. Evaluation of screening for retinopathy of prematurity by ROPtool or a lay reader;Abbey;Ophthalmology,2016

2. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning;Abramoff;Investig. Ophthalmol. Vis. Sci.,2016

3. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices;Abramoff;NPJ Digital Med.,2018

4. Medical image analysis using convolutional neural networks: a review;Anwar;J. Med. Syst.,2018

5. Using Deep Learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images;Asaoka;Am. J. Ophthalmol.,2018

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