Alzheimer’s disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer’s Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection.

阿尔茨海默病 （AD） 是一项全球性的医疗保健挑战，缺乏一种简单且经济实惠的检测方法。我们提出了一种新的深度学习框架 Eye-AD，以使用视网膜微血管系统和脉络膜毛细血管的 OCTA 图像来检测早发性阿尔茨海默病 （EOAD） 和轻度认知障碍 （MCI）。Eye-AD 采用多级图形表示来分析视网膜层中的实例内和实例间关系。使用来自多中心研究中 1671 名参与者的 5751 张 OCTA 图像，我们的模型在 EOAD （内部数据：AUC = 0.9355，外部数据：AUC = 0.9007）和 MCI 检测（内部数据：AUC = 0.8630，外部数据：AUC = 0.8037）方面表现出优异的性能。此外，我们探讨了 OCTA 图像中视网膜结构生物标志物与 EOAD/MCI 之间的关联，结果与我们的深度学习可解释性分析得出的结论非常吻合。我们的研究结果提供了进一步的证据，表明视网膜 OCTA 成像与人工智能相结合，将作为一种快速、无创且负担得起的痴呆检测。