Early detection of dementia, such as Alzheimer's disease (AD) or mild cognitive impairment (MCI), is essential to enable timely intervention and potential treatment. Accurate detection of AD/MCI is challenging due to the high complexity, cost, and often invasive nature of current diagnostic techniques, which limit their suitability for large-scale population screening. Given the shared embryological origins and physiological characteristics of the retina and brain, retinal imaging is emerging as a potentially rapid and cost-effective alternative for the identification of individuals with or at high risk of AD. In this paper, we present a novel PolarNet+ that uses retinal optical coherence tomography angiography (OCTA) to discriminate early-onset AD (EOAD) and MCI subjects from controls. Our method first maps OCTA images from Cartesian coordinates to polar coordinates, allowing approximate sub-region calculation to implement the clinician-friendly early treatment of diabetic retinopathy study (ETDRS) grid analysis. We then introduce a multi-view module to serialize and analyze the images along three dimensions for comprehensive, clinically useful information extraction. Finally, we abstract the sequence embedding into a graph, transforming the detection task into a general graph classification problem. A regional relationship module is applied after the multi-view module to excavate the relationship between the sub-regions. Such regional relationship analyses validate known eye-brain links and reveal new discriminative patterns.

翻译：阿尔茨海默病（AD）或轻度认知障碍（MCI）等痴呆症的早期检测对于实现及时干预和潜在治疗至关重要。当前诊断技术因其高度复杂性、高昂成本及通常具有侵入性，难以准确检测AD/MCI，这限制了其在大规模人群筛查中的适用性。鉴于视网膜与大脑具有共同的胚胎起源和生理特征，视网膜成像正逐渐成为一种潜在快速且经济高效的替代方案，用于识别患有或具有AD高风险的个体。本文提出了一种新颖的PolarNet+模型，利用视网膜光学相干断层扫描血管成像（OCTA）来区分早发性AD（EOAD）、MCI受试者与对照组。我们的方法首先将OCTA图像从笛卡尔坐标系映射到极坐标系，从而实现近似子区域计算，以实施临床医生友好的糖尿病视网膜病变早期治疗研究（ETDRS）网格分析。随后，我们引入一个多视图模块，沿三个维度对图像进行序列化分析，以提取全面且具有临床价值的信息。最后，我们将序列嵌入抽象为图结构，将检测任务转化为通用的图分类问题。在多视图模块之后应用区域关系模块，以挖掘子区域之间的关系。此类区域关系分析验证了已知的眼-脑关联，并揭示了新的判别性模式。