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    Area of Science:

    • Gerontology
    • Artificial Intelligence
    • Neuroscience

    Background:

    • Alzheimer's disease (AD) poses a global health challenge, with early diagnosis crucial for effective intervention.
    • Conventional screening methods are resource-intensive and impractical for widespread community use.
    • AI-driven behavioral analysis, including speech and facial recognition, shows promise for non-invasive cognitive assessment.

    Purpose of the Study:

    • To develop a community-oriented intelligent screening system for cognitive impairment in elderly populations.
    • To introduce CIR-AV, a large-scale Mandarin multimodal dataset for cognitive impairment recognition.
    • To propose DiVA, a novel audio-visual fusion framework for enhanced cognitive assessment.

    Main Methods:

    • Development of CIR-AV, a multimodal dataset with facial and speech data from 574 Chinese older adults.
    • Proposal of the DiVA framework, employing disentangled representation learning and cross-modal attention for audio-visual fusion.
    • Implementation of a trajectory-constrained mechanism and a cross-modal attention-based dynamic fusion (CMF) module.

    Main Results:

    • DiVA achieved an Area Under the Curve (AUC) of 78.66% at the segment level.
    • DiVA demonstrated an accuracy of 79.46% at the subject level.
    • The proposed system significantly outperformed existing state-of-the-art methods in cognitive impairment recognition.

    Conclusions:

    • The DiVA framework provides a cost-efficient and scalable solution for large-scale community screening of cognitive impairment.
    • This AI-driven approach offers a practical method for early dementia detection, especially in resource-limited settings.
    • The system holds significant social and economic value for proactive public health initiatives.