Alzheimer's disease (AD) is pressing global health concerns, for which early diagnosis is critical to effective intervention. However, conventional approaches, including neuropsychological assessments and neuroimaging techniques, are resource-intensive and impractical for community-level screening. In contrast, artificial intelligence-driven behavioral analyses, including speech pattern and facial expression recognition, have demonstrated considerable potential for scalable and non invasive cognitive assessment. This work presents a community-oriented intelligent screening system for cognitive impairment screening in elderly populations. As a foundation, we introduce CIR-AV, the first large-scale Mandarin-based multimodal dataset for cognitive impairment recognition in Chinese older adults, encompassing 574 community-dwelling participants with comprehensive facial expression and speech data. Building upon this resource, we propose DiVA, a disentangled audio-visual fusion framework that decomposes multimodal features into shared and specific representations. A trajectory constrained mechanism enhances representation purity, while a cross-modal attention-based dynamic fusion (CMF) module adaptively balances modality contributions, ensuring robust performance under real-world conditions. Experimental results demonstrate that DiVA achieves an AUC of 78.66% at the segment level and an accuracy of 79.46% at the subject level, significantly outperforming state-of the-art methods. With its cost-efficient and scalable de sign, it is well-suited for large-scale community screening, providing a practical solution for early dementia detection in resource-limited settings with considerable social and economic value.
Feng et al. (Thu,) studied this question.