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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Multi-View Hilbert Curve-Based Hierarchical Information Aggregation for Incomplete Multimodal Alzheimer's Disease

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Neurodegenerative Disease Diagnostics
    • Medical Data Fusion

    Background:

    • Early Alzheimer's disease (AD) diagnosis relies on multi-modal data (neuroimaging, biomarkers, cognitive tests).
    • Data unavailability due to cost, compliance, or risks is a common clinical challenge.
    • Existing AI models (CNNs, Transformers) struggle with heterogeneous medical data and capturing both local/global dependencies.

    Purpose of the Study:

    • To develop a novel hierarchical information aggregation and dynamic fusion (HI-AD) framework for incomplete multi-modal AD diagnosis.
    • To address limitations in current AI architectures for handling high-dimensional, heterogeneous medical data with missing modalities.
    • To improve the robustness and generalizability of early-stage AD screening in diverse clinical settings.

    Main Methods:

    • Developed a HI-AD framework incorporating a multi-view Hilbert curve-guided Mamba block for spatial feature extraction and long-range dependency modeling.
    • Implemented hierarchical spatial feature extraction to preserve spatial continuity and integrate local context from neuroimaging.
    • Utilized a unified mutual information-driven learning objective with active confidence evaluation to balance semantic alignment and modality-specific information, preventing modality collapse.

    Main Results:

    • The HI-AD framework demonstrated superior performance compared to state-of-the-art methods in extensive experiments on real-world datasets.
    • Consistent outperformance was observed across various scenarios with missing data modalities.
    • The method proved effective and generalizable for early-stage AD screening in heterogeneous clinical data environments.

    Conclusions:

    • The proposed HI-AD framework offers an effective and robust solution for Alzheimer's disease diagnosis using incomplete multi-modal data.
    • The novel approach successfully handles data heterogeneity and missing modalities, outperforming existing methods.
    • HI-AD establishes a promising direction for improving early AD detection in resource-limited or complex clinical settings.