Alzheimer’s disease (AD) is a neurodegenerative disease, well-known for its incurability, and is common among the elderly population worldwide. Previous studies have demonstrated that early intervention positively influences disease progression, leading to increased research into pathological analysis and disease trajectory prediction through machine learning (ML) methods. Given the similarities across different neurodegenerative disorders, a diagnosis relying solely upon a single modality of data is inadequate. Consequently, current research predominantly focuses on multimodal analysis, integrating medical imaging and clinical patient information, with continuous identification of new data types potentially aiding AD diagnosis. Multimodal approaches have been explored extensively over the past two decades, with significant advances observed following the introduction of Deep Learning (DL) techniques. Deep neural networks can adaptively extract and fuse features directly from input data, significantly broadening the scope of multimodal analysis. However, earlier classification studies have primarily concentrated on traditional ML, often neglecting the rapid advancements in DL networks. This article provides a comprehensive description of the acquisition pathways based on modalities, discusses the modalities currently used for research in neuroimaging, human body fluids, and other relevant sources. Additionally, it classifies fusion methodologies utilised in both DL and traditional ML contexts, highlights existing challenges, and outlines potential directions for future research.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)