Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee
Macdonald, Trystan Zhelev, Zhivko Liu, Xiaoxuan Hyde, Christopher Fajtl, Jiri Egan, Catherine Tufail, Adnan Rudnicka, Alicja R Shinkins, Bethany Given-Wilson, Rosalind
Macdonald, Trystan
Zhelev, Zhivko
Liu, Xiaoxuan
Hyde, Christopher
Fajtl, Jiri
Egan, Catherine
Tufail, Adnan
Rudnicka, Alicja R
Shinkins, Bethany
Given-Wilson, Rosalind
Affiliation
University of Birmingham; University Hospitals Birmingham NHS Foundation Trust; University of Exeter; Kingston University London; Moorfields Eye Hospital NHS Foundation Trust; University College London; St George's University of London; University of Warwick; St George's University Hospitals NHS Foundation Trust; Vaccination and Screening Directorate; Gloucestershire Hospitals NHS Foundation Trust; UK National Screening Committee
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Publication date
2025-04-03
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Abstract
Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with diabetes, because of early detection and treatment of sight-threatening disease. There is long-standing interest in the possibility of automating parts of this process through artificial intelligence, commonly known as automated retinal imaging analysis software (ARIAS). A number of such products are now on the market. In the UK, Scotland has used a rules-based autograder since 2011, but the diabetic eye screening programmes in the rest of the UK rely solely on human graders. With more sophisticated machine learning-based ARIAS now available and greater challenges in terms of human grader capacity, in 2019 the UK's National Screening Committee (NSC) was asked to consider the modification of diabetic eye screening in England with ARIAS. Following up on a review of ARIAS research highlighting the strengths and limitations of existing evidence, the NSC here sets out their considerations for evaluating evidence to support the introduction of ARIAS into the diabetic eye screening programme.
Citation
Macdonald T, Zhelev Z, Liu X, Hyde C, Fajtl J, Egan C, Tufail A, Rudnicka AR, Shinkins B, Given-Wilson R, Dunbar JK, Halligan S, Scanlon P, Mackie A, Taylor-Philips S, Denniston AK. Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee. Lancet Digit Health. 2025 May;7(5):100840. doi: 10.1016/j.landig.2024.12.004. Epub 2025 Apr 3.
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Article