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
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Le résumé fourni par la source
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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- 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
- Date Crossref
- 25/08/2026
- Éditeur
- Frontiers Media SA
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Ochsner Health System pays non établi dans la noticeÉtablissement de santé
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The University of Queensland Queensland Medical Program pays non établi dans la noticeUniversité ou école supérieure
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New York Eye and Ear Infirmary pays non établi dans la noticeÉtablissement de santé
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Icahn School of Medicine at Mount Sinai New York Eye and Ear of Mount Sinai pays non établi dans la noticeUniversité ou école supérieure
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AEYE Health Inc pays non établi dans la noticeEntreprise
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A. Stein – Regulatory Affairs Consulting Ltd pays non établi dans la noticeEntreprise
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Department of Ophthalmology pays non établi dans la noticeInstitution
Ochsner Health System, Queensland Medical Program — The University of Queensland et New York Eye and Ear Infirmary, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.