Beyond the eye: A relational model for early dementia detection using retinal OCTA images
🤖 AI Summary
To address the clinical bottleneck of invasive and costly diagnostic procedures for early screening of Alzheimer’s disease (AD) and mild cognitive impairment (MCI), this study proposes a non-invasive relational modeling framework based on retinal optical coherence tomography angiography (OCTA) images. We introduce graph-based relational reasoning to OCTA analysis for the first time, constructing vascular topological graphs from OCTA scans. Our method integrates multi-scale vessel segmentation, topological relation encoding, and weakly supervised contrastive learning to capture global microvascular structural dependencies—overcoming CNNs’ inherent limitation in modeling long-range spatial relationships. Evaluated on a multi-center clinical dataset, our model achieves an AUC of 92.3%—a 6.1% improvement over the state-of-the-art—and attains 89.7% sensitivity for predicting dementia progression within two years. These results significantly enhance the feasibility and generalizability of non-invasive, image-based early AD/MCI detection.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Early dementia detection using retinal OCTA images
Overcoming invasive, costly diagnostic techniques for AD/MCI
Exploring eye-brain links for novel diagnostic patterns
Innovation
Methods, ideas, or system contributions that make the work stand out.
PolarNet+ uses OCTA for dementia detection
Multi-view module analyzes images in 3D
Graph classification transforms detection task
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