Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data

arXiv:2606.11794v1 Announce Type: new Abstract: Neurodegenerative diseases such as Alzheimer's disease (AD) require accurate and scalable tools for assessing disease severity, yet current clinical staging remains time-intensive and prone to variability. We propose an attention-enhanced multimodal machine learning framework with ordinal regression for automated and interpretable AD severity staging. The framework integrates T1-weighted MRI with demographic and genetic variables and compares unimodal and multimodal architectures using ordinal and non-ordinal prediction heads. Models were trained

Source: arXiv cs.LG — read the full report at the original publisher.

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