A Multimodal Approach to Alzheimer's Diagnosis: Geometric Insights from Cube Copying and Cognitive Assessments
arXiv:2512.16184v2 Announce Type: replace Abstract: 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 spati
The paper leverages recent advancements in AI, particularly multimodal approaches and graph neural networks, to address a long-standing challenge in medical diagnostics.
Early and accessible detection of Alzheimer's disease has significant implications for treatment efficacy, patient quality of life, and healthcare system burden.
This research introduces a novel, potentially low-cost, and non-invasive AI-driven method for AD diagnosis, moving beyond traditional subjective assessments of drawing tasks.
- · AI in healthcare
- · Medical diagnostics companies
- · Neurology patients reliant on early detection
- · Geriatric care providers
- · Traditional, purely subjective cognitive assessment methods
- · Late-stage AD treatment developers (if early detection becomes widespread)
This technology could significantly improve the accuracy and accessibility of Alzheimer's disease screening.
Widespread adoption might lead to earlier interventions, potentially altering disease progression and reducing long-term healthcare costs.
The success of integrating visuospatial and cognitive AI analytics could spur similar multimodal AI applications across other neurological and psychiatric conditions.
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Read at arXiv cs.LG