Non-Invasive Techniques for Early Alzheimer’s Disease Detection: A Survey.

The article surveys non-invasive techniques for early detection of Alzheimer's Disease (AD), emphasizing computer vision and artificial intelligence (AI) applications. It reviews advances in neuroimaging methods (e.g., MRI, PET, fNIRS) enhanced by deep learning for structural and functional brain analysis, alongside emerging AI-driven approaches analyzing speech, facial expressions, gait, and human activity patterns to identify subtle cognitive and motor changes indicative of early AD. The paper highlights the use of machine learning models on multimodal datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) and DementiaBank, discusses challenges including data variability and privacy, and underscores the potential of integrating wearable sensors and video-based monitoring in smart home environments to support diagnosis and patient care. Future directions call for improved model interpretability, larger diverse datasets, and ethically responsible deployment to enhance early diagnosis and intervention outcomes.