ArticleOphthalmology. Retina2021
Diabetic Retinopathy Screening with Automated Retinal Image Analysis in a Primary Care Setting Improves Adherence to Ophthalmic Care.
Article in Ophthalmology. Retina, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 5 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Diabetic retinopathy treatment cascade and care continuum in the USA: a systematic review.BMJ open · 2026Pooled it
- Diabetic Retinopathy Screening Approaches in Developing Countries: A Systematic Review and Meta-Analysis.Turkish journal of ophthalmology · 2025Pooled it
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review.JAMA network open · 2024Pooled it
- Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review.Journal of medical screening · 2023Pooled it
- Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images.Frontiers in artificial intelligence · 2026Trial
- Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial.Nature communications · 2024Trial
- Early Detection of Glaucoma and Diabetic Retinopathy in Low-Resource Settings: Barriers and Solutions.Journal of clinical medicine · 2026Review
- Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review.The British journal of ophthalmology · 2026Article
- Evaluation of AI tool assisting primary healthcare physicians to diagnostic and treatment tasks.Family medicine and community health · 2026Article
- Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Reliability-aware fusion of ocular thermal imaging and radiometric features for proof-of-concept classification of diabetic retinopathy vs. healthy controls.Frontiers in medicine · 2026Article
- Autonomous Artificial Intelligence in Diabetic Retinopathy Testing-Lessons Learned on Successful Health System Adoption.Ophthalmology science · 2026Article
- Artificial intelligence in preventive care in primary health care settings: a scoping review.Archives of medical sciences. Atherosclerotic diseases · 2026Article
- Machine learning versus traditional regression models for predicting diabetic retinopathy screening adherence among community-dwelling older adults with diabetes.Frontiers in endocrinology · 2026Article
- A clinically interpretable machine learning model for early detection of diabetic retinopathy in multiple community health centers.Frontiers in endocrinology · 2026Article
- Diabetic Retinopathy Screening Among Federally Qualified Health Center Patients Using Point-of-Care AI: DRES-POCAI: A Trial Protocol.JAMA network open · 2025Article
- Identifying knowledge gap and adherence barriers for diabetic retinopathy screening: Findings from primary healthcare clinics in Pakistan.Journal of family medicine and primary care · 2025Article
- Code-Free Machine Learning for the Detection of Common Ophthalmic Diseases.Translational vision science & technology · 2025Article
- AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
purposeRetinal screening examinations can prevent vision loss resulting from diabetes but are costly and highly underused. We hypothesized that artificial intelligence-assisted nonmydriatic point-of-care screening administered during primary care visits would increase the adherence to recommendations for follow-up eye care in patients with diabetes.
designProspective cohort study.
participantsAdults 18 years of age or older with a clinical diagnosis of diabetes being cared for in a metropolitan primary care practice for low-income patients.
methodsAll participants underwent nonmydriatic fundus photography followed by automated retinal image analysis with human supervision. Patients with positive or inconclusive screening results were referred for comprehensive ophthalmic evaluation. Adherence to referral recommendations was recorded and compared with the historical adherence rate from the same clinic.
main outcome measureRate of adherence to eye screening recommendations.
resultsBy automated screening, 8.3% of the 180 study participants had referable diabetic eye disease, 13.3% had vision-threatening disease, and 29.4% showed inconclusive results. The remaining 48.9% showed negative screening results, confirmed by human overread, and were not referred for follow-up ophthalmic evaluation. Overall, the automated platform showed a sensitivity of 100% (confidence interval, 92.3%-100%) in detecting an abnormal screening results, whereas its specificity was 65.7% (confidence interval, 57.0%-73.7%). Among patients referred for follow-up ophthalmic evaluation, the adherence rate was 55.4% at 1 year compared with the historical adherence rate of 18.7% (P < 0.0001, Fisher exact test).
conclusionsImplementation of an automated diabetic retinopathy screening system in a primary care clinic serving a low-income metropolitan patient population improved adherence to follow-up eye care recommendations while reducing referrals for patients with low-risk features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.