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Alzheimer's Disease (AD) is a progressive neurodegenerative disease and theleading cause of dementia. Early diagnosis is critical for patients to benefitfrom potential intervention and treatment. The retina has been hypothesized asa diagnostic site for AD detection owing to its anatomical connection with thebrain. Developed AI models for this purpose have yet to provide a rationalexplanation about the decision and neither infer the stage of disease'sprogression. Along this direction, we propose a novel model-agnosticexplainable-AI framework, called Granular Neuron-level Explainer (LAVA), aninterpretation prototype that probes into intermediate layers of theConvolutional Neural Network (CNN) models to assess the AD continuum directlyfrom the retinal imaging without longitudinal or clinical evaluation. Thismethod is applied to validate the retinal vasculature as a biomarker anddiagnostic modality for Alzheimer's Disease (AD) evaluation. UK Biobankcognitive tests and vascular morphological features suggest LAVA shows strongpromise and effectiveness in identifying AD stages across the progressioncontinuum.

LAVA: Granular Neuron-Level Explainable AI for Alzheimer's Disease Assessment from Fundus Images