发表机构
Columbia University; Central Michigan University（哥伦比亚大学; 中密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出整合血管分割、生物标志物提取与LLM报告的OCTA流程，基于ROSE-1数据集识别AD相关视网膜表型，为AD研究提供透明的非诊断性证据关联解释。
AI 中文摘要
阿尔茨海默病（AD）的早期识别仍具挑战性，因为已有的评估方法成本高、资源密集或不适用于人群规模筛查。光学相干断层扫描血管造影（OCTA）可无创可视化视网膜微血管，但现有方法常需诊断标签且测量级解释有限。本文提出一种可解释的OCTA流程，整合了感知注释的血管分割、分层血管生物标志物提取、无标签表型分析及基于测量的大语言模型（LLM）报告。使用来自39名受试者的117张ROSE-1图像，将注释匹配的分割模型应用于浅层血管复合体（SVC）、深层血管复合体（DVC）及SVC+DVC组合表征，模型的ROC-AUC值为0.916-0.970，Dice分数为0.695-0.781。6种密度和分形维数生物标志物构成受试者水平特征用于探索性聚类，对9名保留受试者的分析识别出内部一致的低密度、低分形维数表型，但因缺乏诊断标签无法进行临床解释。对使用GPT、Gemini和Llama生成的报告从测量关联性、引用忠实性和诊断谨慎性三方面进行评估。总体而言，该框架为阿尔茨海默病研究提供了视网膜血管测量、探索性表型分析与证据关联解释之间的透明、非诊断性连接。
英文摘要
Early identification of Alzheimer's disease (AD) remains challenging because established assessment methods can be costly, resource-intensive, or unsuitable for population-scale screening. Optical coherence tomography angiography (OCTA) provides non-invasive visualization of retinal microvasculature, but existing approaches often require diagnostic labels and provide limited measurement-level interpretation. We present an explainable OCTA pipeline that integrates annotation-aware vessel segmentation, layer-specific vascular biomarker extraction, label-free phenotyping, and measurement-grounded LLM reporting. Using 117 ROSE-1 images from 39 subjects, we apply annotation-matched segmentation models to superficial vascular complex (SVC), deep vascular complex (DVC), and combined SVC+DVC representations. The models achieve ROC-AUC values of 0.916-0.970 and Dice scores of 0.695-0.781. Six density and fractal-dimension biomarkers form subject-level profiles for exploratory clustering. Analysis of nine held-out subjects identifies an internally consistent lower-density, lower-fractal-dimension phenotype, although the absence of diagnostic labels prevents clinical interpretation. Reports generated using GPT, Gemini, and Llama are evaluated for measurement grounding, citation faithfulness, and diagnostic caution. Overall, the framework provides a transparent, non-diagnostic connection between retinal vascular measurements, exploratory phenotyping, and evidence-linked interpretation for Alzheimer's research.
Comments4th IEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, (AIBThings)
