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

Background/aimsHuman 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 En...

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Vydané v:British journal of ophthalmology Ročník 105; číslo 5; s. 723 - 728
Hlavní autori: Heydon, Peter, Egan, Catherine, Bolter, Louis, Chambers, Ryan, Anderson, John, Aldington, Steve, Stratton, Irene M, Scanlon, Peter Henry, Webster, Laura, Mann, Samantha, du Chemin, Alain, Owen, Christopher G, Tufail, Adnan, Rudnicka, Alicja Regina
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England BMJ Publishing Group LTD 01.05.2021
BMJ Publishing Group
Predmet:
Algorithms
Artificial intelligence
Automation
Clinical Science
Cost analysis
Diabetes
Diabetic retinopathy
Epidemiology
Machine learning
Medical diagnosis
Patient care planning
Public health
Software upgrading
Telemedicine
United Kingdom > UK
Treatment Medical
Clinical Trial
Telemedicine
Imaging
Diagnostic tests/Investigation
Retina
Degeneration
Medical Education
Epidemiology
Public health
ISSN:0007-1161, 1468-2079, 1468-2079
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Shrnutí:Background/aimsHuman 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.MethodsRetinal 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.ResultsSensitivity (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.ConclusionThe 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.
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ISSN:0007-1161
1468-2079
1468-2079
DOI:10.1136/bjophthalmol-2020-316594