Summary
AlzEye represents a large-scale resource linking longitudinal multimodal retinal imaging from routine NHS ophthalmic care with nationally recorded hospital admission data for over 353,000 patients in London. This cohort, enriched with subpopulations affected by systemic disease, provides a rare large-labelled dataset to support development of deep-learning models for oculomics—the detection of systemic disease signatures from retinal imaging. The study exemplifies a privacy-by-design approach to record-level linkage of routine clinical data across health service boundaries.
UK applicability
This study is directly applicable to UK healthcare, being conducted within the NHS at Moorfields Eye Hospital and utilising nationally collected Hospital Episode Statistics. The methodology and findings could inform development of retinal imaging-based screening or prognostication tools for systemic diseases within NHS ophthalmology and primary care settings.
Key measures
Number of participants with retinal imaging (154,830); total retinal images acquired (6,261,931); hospital admissions (1,337,711 episodes); prevalence of systemic diagnoses (myocardial infarction n=12,022; stroke n=11,735; dementia n=13,363); distribution of retinal image modalities
Outcomes reported
The study linked longitudinal multimodal retinal imaging from NHS routine ophthalmic care with hospital admission data for systemic diseases including myocardial infarction, stroke, and dementia in a cohort of over 353,000 patients aged 40+ years. The dataset comprised over 6.2 million retinal images of seven modalities acquired from over 154,000 patients, with hospital episode statistics capturing 1.3 million admission episodes.
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