ArticleEye (London, England)2026
I-SCREEN: Development of an AI-based infrastructure for community-wide screening and prediction of progression in age-related macular degeneration providing accessible shared care.
Article in Eye (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Corrections and comments
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19 authors.
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Abstract
objectivesThis work describes the design and methodological framework of the I-SCREEN project, which aims to develop an artificial intelligence (AI)-based infrastructure utilising optical coherence tomography (OCT) for early detection of AMD and assessment of progression risk.
methodsThe pan-European project is conducted across clinics and optometry/optician practices in six European countries. I-SCREEN encompasses seven work packages covering community-based AMD identification, clinical follow-up, AI development and project dissemination. Three interconnected clinical studies are carried out by optometry/optician practices (PYRENEES) and ophthalmology clinics (SUDETES and APENNINES).
resultsThe PYRENEES study is a prospective, cross-sectional study evaluating the feasibility of detecting subclinical AMD in optometry/optician practices under ophthalmologist supervision via telemedicine. A robust screening network comprising 28 community-based optometry/optician practices and 7 ophthalmology clinics has been established. Patients with suspected non-neovascular AMD are referred to partnered clinics. In the hospital setting, patients with early or intermediate AMD are followed in the longitudinal SUDETES study, while patients with non-foveal geographic atrophy are invited to take part in the APENNINES study. Data obtained inform AI development for community-based AMD detection and monitoring. Predictive modelling will further enable personalised risk assessments.
conclusionsI-SCREEN brings together multidisciplinary experts across Europe to establish an AI-driven shared care model for AMD detection and monitoring. By combining high-quality OCT imaging from community practices with longitudinal clinical studies, the initiative provides novel insights into early AMD progression and establishes a foundation for innovative AI-based detection and prediction throughout the real-world population.
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