Artificial intelligence for diabetic retinopathy screening: a review

Andrzej Grzybowski; Piotr Brona; Gilbert Lim; Paisan Ruamviboonsuk; Gavin S. W Tan; Michael Abramoff; Daniel S. W Ting

doi:10.1038/s41433-019-0566-0

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Artificial intelligence for diabetic retinopathy screening: a review

Journal article

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Peer reviewed

Artificial intelligence for diabetic retinopathy screening: a review

Andrzej Grzybowski, Piotr Brona, Gilbert Lim, Paisan Ruamviboonsuk, Gavin S. W Tan, Michael Abramoff and Daniel S. W Ting

Eye (London), Vol.34(3), pp.451-460

03/2020

DOI: 10.1038/s41433-019-0566-0

PMID: 31488886

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Abstract

Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing population, this poses significant challenge to perform diabetic retinopathy (DR) screening for these patients. Artificial intelligence (AI) using machine learning and deep learning have been adopted by various groups to develop automated DR detection algorithms. This article aims to describe the state-of-art AI DR screening technologies that have been described in the literature, some of which are already commercially available. All these technologies were designed using different training datasets and technical methodologies. Although many groups have published robust diagnostic performance of the AI algorithms for DR screening, future research is required to address several challenges, for examples medicolegal implications, ethics, and clinical deployment model in order to expedite the translation of these novel technologies into the healthcare setting.

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Title: Subtitle: Artificial intelligence for diabetic retinopathy screening: a review
Creators: Andrzej Grzybowski - Olsztyn, Poland
Piotr Brona - Olsztyn, Poland
Gilbert Lim - Singapore, Singapore
Paisan Ruamviboonsuk - Bangkok, Thailand
Gavin S. W Tan - Singapore, Singapore
Michael Abramoff - Iowa City, Iowa USA
Daniel S. W Ting - Singapore, Singapore
Resource Type: Journal article
Publication Details: Eye (London), Vol.34(3), pp.451-460
DOI: 10.1038/s41433-019-0566-0
PMID: 31488886
NLM abbreviation: Eye (Lond)
ISSN: 0950-222X
eISSN: 1476-5454
Publisher: Nature Publishing Group UK; London
Language: English
Date published: 03/2020
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Fraternal Order of Eagles Diabetes Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984060741202771

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