Automated Diabetic Retinopathy Image Assessment Software
Open accessAbstract
OBJECTIVE: With the increasing prevalence of diabetes, annual screening for diabetic retinopathy (DR) by expert human grading of retinal images is challenging. Automated DR image assessment systems (ARIAS) may provide clinically effective and cost-effective detection of retinopathy. We aimed to determine whether ARIAS can be safely introduced into DR screening pathways to replace human graders. DESIGN: Observational measurement comparison study of human graders following a national screening program for DR versus ARIAS. PARTICIPANTS: Retinal images from 20 258 consecutive patients attending routine annual diabetic eye screening between June 1, 2012, and November 4, 2013. METHODS: Retinal images were manually graded following a standard national protocol for DR screening and were processed by 3 ARIAS: iGradingM, Retmarker, and EyeArt. Discrepancies between manual grades and ARIAS results were sent to a reading center for arbitration. MAIN OUTCOME MEASURES: Screening performance (sensitivity, false-positive rate) and diagnostic accuracy (95% confidence intervals of screening-performance measures) were determined. Economic analysis estimated the cost per appropriate screening outcome. RESULTS: Sensitivity point estimates (95% confidence intervals) of the ARIAS were as follows: EyeArt 94.7% (94.2%-95.2%) for any retinopathy, 93.8% (92.9%-94.6%) for referable retinopathy (human graded as either ungradable, maculopathy, preproliferative, or proliferative), 99.6% (97.0%-99.9%) for proliferative retinopathy; Retmarker 73.0% (72.0 %-74.0%) for any retinopathy, 85.0% (83.6%-86.2%) for referable retinopathy, 97.9% (94.9%-99.1%) for proliferative retinopathy. iGradingM classified all images as either having disease or being ungradable. EyeArt and Retmarker saved costs compared with manual grading both as a replacement for initial human grading and as a filter prior to primary human grading, although the latter approach was less cost-effective. CONCLUSIONS: Retmarker and EyeArt systems achieved acceptable sensitivity for referable retinopathy when compared with that of human graders and had sufficient specificity to make them cost-effective alternatives to manual grading alone. ARIAS have the potential to reduce costs in developed-world health care economies and to aid delivery of DR screening in developing or remote health care settings.
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Similar cost-effectiveness of autonomous AI testing has been demonstrated in Europe.
“Similar cost-effectiveness has been demonstrated in Europe.64”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [64]
The Health Economic Analysis evaluates automated diabetic-retinopathy screening in the UK, and the Results report lower costs than manual grading. But the Results also find that both systems were less likely to classify disease correctly; the paper does not establish unqualified cost-effectiveness. (LLM, conf=0.85)
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- Authors
- Tufail, A., Rudisill, C., Egan, C., Kapetanakis, V. V., Salas-Vega, S., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., Bolter, L., Srinivas, S., Nittala, M., Sadda, S., Taylor, P., & Rudnicka, A. R.
- Affiliations
- NIHR Moorfields Biomedical Research Centre (GB)
- London School of Economics and Political Science (GB)
- St George's, University of London (GB)
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- Homerton University Hospital (GB)
- Doheny Eye Institute (US)
- University College London (GB)
- Corresponding author
- Tufail, Adnan
- ORCID
- Venue
- Ophthalmology
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 124
- Issue
- 3
- Pages
- 343-351
- ISSN
- 0161-6420 (ISSN-L)
- OpenAlex
- W2561588396
- Cited by
- 283
- References
- 20
- Citations by year
- 2026: 27 · 2025: 45 · 2024: 38 · 2023: 33 · 2022: 18 · 2021: 36 · 2020: 33 · 2019: 26 · 2018: 17 · 2017: 9 · 2012: 1
- FWCI
- 15.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Ophthalmology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- London School of Economics and Political Science Theses Online (London School of Economics and Political Science) (acceptedVersion)
- St George's Online Research Archive (St George's University of London) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Regeneron Pharmaceuticals
- National Institute for Health and Care Research (11/21/02)
- Carl Zeiss Meditec AG
- Heidelberg Engineering
- Moorfields Eye Hospital NHS Foundation Trust
- Genentech
- Allergan
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- diabetic retinopathy, retinal image analysis, diabetic retinopathy screening, proliferative diabetic retinopathy, diagnostic accuracy, cost-effectiveness, screening sensitivity and specificity, preproliferative diabetic retinopathy
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Cost-Benefit Analysis, Decision Trees, Diabetic Retinopathy, Economics, Medical, False Negative Reactions, Female, Humans, Image Interpretation, Computer-Assisted, Male, Mass Screening, Middle Aged, Physical Examination, Predictive Value of Tests, Sensitivity and Specificity, Software, Reproducibility of Results
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