Early and accessible detection of Alzheimer's disease (AD) remains a critical clinical challenge, and cube-copying tasks offer a simple yet informative assessment of visuospatial function. This work proposes a multimodal framework that converts hand-drawn cube sketches into graph-structured representations capturing geometric and topological properties, and integrates these features with demographic information and neuropsychological test (NPT) scores for AD classification. Cube drawings are modeled as graphs with node features encoding spatial coordinates, local graphlet-based topology, and angular geometry, which are processed using graph neural networks and fused with age, education, and NPT features in a late-fusion model. Experimental results show that graph-based representations provide a strong unimodal baseline and substantially outperform pixel-based convolutional models, while multimodal integration further improves performance and robustness to class imbalance. SHAP-based interpretability analysis identifies specific graphlet motifs and geometric distortions as key predictors, closely aligning with clinical observations of disorganized cube drawings in AD. Together, these results establish graph-based analysis of cube copying as an interpretable, non-invasive, and scalable approach for Alzheimer's disease screening.
Yang et al. (Thu,) studied this question.