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.
Funder
NIHR Applied
Research Collaboration South London
NIHR
Biomedical Research Centre for Ophthalmology at Moorfields Eye Hospital
and UCL Institute of Ophthalmology
Subject
Cellular and Molecular Neuroscience,Sensory Systems,Ophthalmology
Cited by
108 articles.
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