  Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the
leading cause of dementia. Early diagnosis is critical for patients to benefit
from potential intervention and treatment. The retina has been hypothesized as
a diagnostic site for AD detection owing to its anatomical connection with the
brain. Developed AI models for this purpose have yet to provide a rational
explanation about the decision and neither infer the stage of disease's
progression. Along this direction, we propose a novel model-agnostic
explainable-AI framework, called Granular Neuron-level Explainer (LAVA), an
interpretation prototype that probes into intermediate layers of the
Convolutional Neural Network (CNN) models to assess the AD continuum directly
from the retinal imaging without longitudinal or clinical evaluation. This
method is applied to validate the retinal vasculature as a biomarker and
diagnostic modality for Alzheimer's Disease (AD) evaluation. UK Biobank
cognitive tests and vascular morphological features suggest LAVA shows strong
promise and effectiveness in identifying AD stages across the progression
continuum.
