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Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients.

Heydon, P; Egan, C; Bolter, L; Chambers, R; Anderson, J; Aldington, S; Stratton, IM; Scanlon, PH; Webster, L; Mann, S; et al. Heydon, P; Egan, C; Bolter, L; Chambers, R; Anderson, J; Aldington, S; Stratton, IM; Scanlon, PH; Webster, L; Mann, S; du Chemin, A; Owen, CG; Tufail, A; Rudnicka, AR (2021) Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol, 105 (5). pp. 723-728. ISSN 1468-2079 https://doi.org/10.1136/bjophthalmol-2020-316594
SGUL Authors: Owen, Christopher Grant Rudnicka, Alicja Regina

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Abstract

BACKGROUND/AIMS: Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard. METHODS: Retinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard. RESULTS: Sensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy. CONCLUSION: The algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed.

Item Type: Article
Additional Information: © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
Keywords: Clinical Trial, Degeneration, Diagnostic tests/Investigation, Epidemiology, Imaging, Medical Education, Public health, Retina, Telemedicine, Treatment Medical, 1103 Clinical Sciences, 1113 Opthalmology and Optometry, 1117 Public Health and Health Services, Ophthalmology & Optometry
SGUL Research Institute / Research Centre: Academic Structure > Population Health Research Institute (INPH)
Journal or Publication Title: Br J Ophthalmol
ISSN: 1468-2079
Language: eng
Dates:
DateEvent
22 April 2021Published
30 June 2020Published Online
28 May 2020Accepted
Publisher License: Creative Commons: Attribution-Noncommercial 4.0
Projects:
Project IDFunderFunder ID
NIHR200152National Institute for Health Researchhttp://dx.doi.org/10.13039/501100000272
PubMed ID: 32606081
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/112123
Publisher's version: https://doi.org/10.1136/bjophthalmol-2020-316594

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