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.

翻译：阿尔茨海默病（AD）是一种进行性神经退行性疾病，也是导致痴呆症的主要原因。早期诊断对于患者获得潜在的干预和治疗至关重要。由于视网膜与大脑存在解剖学联系，其已被假设为AD检测的潜在诊断部位。然而，为此开发的AI模型既未能提供关于决策的合理解释，也无法推断疾病进展的阶段。针对这一问题，我们提出了一种新颖的模型无关的可解释人工智能框架，称为颗粒神经元级解释器（LAVA）——一种探索卷积神经网络（CNN）模型中间层，无需纵向或临床评估即可直接从视网膜影像评估AD连续谱的解释原型。该方法用于验证视网膜血管作为阿尔茨海默病（AD）评估的生物标志物和诊断方式的可靠性。英国生物样本库的认知测试与血管形态学特征表明，LAVA在识别AD病程连续谱中的不同阶段方面展现出显著的潜力和有效性。