Ophthalmologists from Moorfields Eye Hospital have been part of a research project that has used artificial intelligence (AI) to detect eye disease among patients with diabetes.
Published in the British Journal of Ophthalmology, the study uses the images from 30,000 patient scans (120,000 images), using AI to look for signs of damage.
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 diabetes related eye disease that could lead to vision loss.
The researchers found that the use of AI in diabetic eye screening could potentially save more than £10 million every year in England alone. The team of researchers, who were also from St George’s, University of London, UCL, Homerton University Hospital, Gloucestershire Hospitals and Guy’s and St Thomas’ NHS Foundation Trusts, hope the research will enable systematic changes in the UK’s national screening programme.
As well as having potential implications for this screening programme these findings could be used to monitor diabetic eye disease in other countries, reducing the risk of vision loss on a wider scale.
Professor Adnan Tufail, consultant ophthalmologist from Moorfields Eye Hospital and the Institute of Ophthalmology, UCL, said:
“Most AI software is tested by the developers or companies themselves. What is so important about this pivotal study is it uses data from across the country, has a large sample size of more than 120,000 images of real-world patients and was run independently.”
“We have shown that this validated AI software can reduce the burden of humans needing to grade diabetic eye screening images in the UK massively, by more than 5 million images per year. The technology is incredibly fast, does not miss a single case of severe diabetic retinopathy and could contribute to healthcare system recovery post-COVID.”
“We have developed what we believe to be a gold standard study of how to validate AI for clinical use. We hope that other countries will follow this methodology, giving healthcare systems confidence in using this technology.”
