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NEWS
REPORT
AI could prevent sight loss
Ways of seeing
A
rtificial intelligence
(AI) technology can
accurately detect
retinopathy and could
halve the human
workload associated with screening for
diabetic eye disease, saving millions of
pounds every year, according to new
research.
The findings, from the largest study
of AI use in the English Diabetic Eye
Screening Programme (DESP), could
also pave the way for the technology to
be used to reduce the backlog in eye
screening appointments following Covid19 lockdown.
The study used images from 30,000
patient scans (120,000 images) in the
DESP to look for signs of damage
using the EyeArt artificial intelligence eye
screening technology. The results showed
that the technology has 95.7% accuracy
for detecting damage that would require referral to specialist services, but
100% accuracy for moderate to severe
retinopathy or serious disease that could
lead to vision loss.
The DESP is set up to screen diabetic
patients once a year for signs of damage
that could potentially lead to sight loss.
The researchers found that the use of the
EyeArt machine learning technology could
potentially save £0.5 million per 100,000
screening episodes. With more than 2.2
million screening episodes per year, the
savings could extend to more than £10
million every year in England alone.
The researchers from St George's,
University of London, Moorfields Eye
Hospital, UCL, and Homerton University
Hospital, Gloucestershire Hospitals and
Guy's and St Thomas' NHS Foundation
Trusts hope the research will enable
systematic changes in the UK national
screening programme.
Professor Alicja Rudnicka, senior
author on the paper, from St George's,
University of London, said: "The national
screening programme has been shown to
be highly effective at reducing the levels
of sight loss due to diabetes. Damage
to the eye is easily detectable, and we
have effective treatments available for
those that need it. But there is a very high
burden on human graders required to
diagnose the thousands of images every
day-most of which show no signs of
disease and require no further action.
"Our study shows that machine
learning technology could safely halve
the number of images that need to be
assessed by humans, freeing up further
funds and resources for the NHS. If this
technology is rolled out on a national level,
it could immediately reduce the backlog
of cases created due to the coronavirus
pandemic, potentially saving unnecessary
vision loss in the diabetic population."
As well as having potential implications
for the screening programme in the UK,
these findings could be promising in
other countries without a workforce set
up to detect the disease. Global cases
of diabetes are expected to rise to 629
million by 2045, and the technology could
be used to monitor the diabetic population
in many countries, referring people to
specialist services if they are found to be
at risk of sight loss.
The study is published in the British
Journal of Ophthalmology.
machine learning
technology could
safely halve the
number of images that
need to be assessed
by humans, freeing up
funds and resources
"