LAVA: Granular Neuron-Level Explainable AI for Alzheimer's Disease Assessment from Fundus Images
This addresses the need for non-invasive, explainable diagnostic tools for Alzheimer's Disease patients, though it appears incremental as it builds on existing hypotheses about retinal biomarkers.
The paper tackled the problem of early Alzheimer's Disease diagnosis by proposing LAVA, a model-agnostic explainable-AI framework that assesses AD stages from retinal fundus images, showing strong promise in identifying progression stages without longitudinal data.
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.