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ISSUE 88

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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

"

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