Autonomous AI-driven point-of-care screening for diabetic retinopathy compared to reading center multi-expert clinical review: results from three prospective controlled pivotal validation studies with AEYE-DS in over 1,200 patients
In brief
AI system detects more-than-mild diabetic retinopathy with 93% sensitivity and 91% specificity
In three prospective studies of over 1,200 patients, the autonomous AEYE-DS algorithm identified more-than-mild diabetic retinopathy with about 93% sensitivity and 91-94% specificity compared with multi-expert reading-center grading. Imageability exceeded 99% and reproducibility was high across handheld and desktop cameras, suggesting the tool could expand point-of-care screening access.
- Journal
- Frontiers in digital health (Q1)
- Published
- 25 August 2026
- Study design
- Narrative review / expert opinion
- Evidence level
- Level 5, Expert Opinion (CEBM 5)
- Authors
- Zack Dvey-Aharon, Moshe Livne, Dan Margalit, Amit Wohl, Adi Haupt, Rachelle Aviv, et al.
- PMID
- 42712598
- DOI
- 10.3389/fdgth.2026.1907804
Why clinicians should know about it
- Picked for Ophthalmology (paper of the day, 14 September 2026): AEYE‑DS AI shows >90% sensitivity/specificity for mtmDR screening
Abstract
PURPOSE: Diabetic retinopathy (DR) remains the leading cause of blindness among working-age adults and requires regular screening to detect progression among the growing global diabetic population. This study evaluated the performance of AEYE-DS, an autonomous artificial intelligence (AI) system designed for high-throughput, point-of-care analysis of retinal images, in detecting more-than-mild diabetic retinopathy (mtmDR) during routine screening of patients with diabetes who had not previously been diagnosed with DR. PRINCIPAL RESULTS: AEYE-DS was tested across three prospective clinical studies using two FDA-cleared non-mydriatic retinal cameras: the handheld Aurora and the desktop Topcon NW400. The algorithm autonomously analyzed retinal images and determined mtmDR presence. Diagnostic outcomes were compared to a reference standard based on the Early Treatment for Diabetic Retinopathy Study (ETDRS) severity grading performed by multi-expert review at an independent reading center. Sensitivity and specificity were 93% and 91% in AEYE-1 (95% CI: 83%-97% and 88%-94%), 92% and 94% in AEYE-2 (95% CI: 79%-97% and 90%-96%), and 93% and 89% in AEYE-3 (95% CI: 80%-97% and 85%-92%). Imageability was >99% in all studies. Intra-operator repeatability exceeded 99% for both devices. Between-operator reproducibility was 98% for the desktop camera and 95% for the handheld device, while between-device reproducibility reached 99% and 97%, respectively. CONCLUSIONS: AEYE-DS demonstrated high diagnostic accuracy, imageability, reliability, and reproducibility across different operators and devices in non-mydriatic settings. Findings support autonomous AI system use for scalable, point-of-care DR screening, potentially expanding access, streamlining workflows, and reducing the global burden of diabetic eye disease.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.