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

TL;DR

LAVA is a novel neuron-level explainable AI framework that interprets CNN decisions to assess Alzheimer's disease stages from retinal images, providing insights into disease progression without clinical data.

Contribution

The paper introduces LAVA, a model-agnostic, neuron-level interpretability method for CNNs applied to retinal images for AD diagnosis and staging.

Findings

LAVA effectively identifies AD stages from retinal vasculature.

Retinal features correlate with AD progression stages.

LAVA demonstrates strong promise in early AD detection.

Abstract

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.