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VRISTA – Voxel-based Retinal Imaging and Systemic-risk Translation with AI across UK and Japanese Cohorts

CORDIS · observation · Publication date unknown

ng, to OCT volumes to produce anatomically aligned deviation maps that quantify where the retina differs from expected patterns. These OCT features will be fused with macro-scale fundus cues within a deep learning model that estimates continuous, calibrated risk and time-to-event outcomes. To enable screening in settings without OCT, we will use knowledge distillation so that a lightweight fundus-only model inherits performance from the full multimodal system. Self-supervised pretraining and foundation-model backbones will support generalisability and data efficiency. The work leverages harmonised UK and Japanese cohorts totalling more than 200,000 images, enabling rigorous external validation and fairness analysis by sex, refractive status, and ethnicity. Transparency will be promoted through saliency visualisation, concept-based probes, and counterfactual testing. By delivering an accurate, explainable, and deployable tool that works with existing eye-care infrastructure, the project addresses European priorities on healthy ageing and prevention, strengthens EU-Japan collaboration,

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recordType

award

status

SIGNED

region

EU

value

276187.92

unit

EUR