A review of multimodal fusion-based deep learning for Alzheimer's disease

Alzheimer's Disease (AD) as one of the most prevalent neurodegenerative disorders worldwide, characterized by significant memory and cognitive decline in its later stages, severely impacting daily lives. Consequently, early diagnosis and accurate assessment are crucial for delaying disease progression. In recent years, multimodal imaging has gained widespread adoption in AD diagnosis and research, particularly the combined use of Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). The complementarity of these modalities in structural and metabolic information offers a unique advantage for comprehensive disease understanding and precise diagnosis. With the rapid advancement of deep learning techniques, efficient fusion of MRI and PET multimodal data has emerged as a prominent research focus. This review systematically surveys the latest advancements in deep learning-based multimodal fusion of MRI and PET images for AD research, with a particular focus on studies published in the past five years (2021-2025). It first introduces the main sources of AD-related data, along with data preprocessing and feature extraction methods. Then, it summarizes performance metrics and multimodal fusion techniques. Next, it explores the application of various deep learning models and their variants in multimodal fusion tasks. Finally, it analyzes the key challenges currently faced in the field, including data scarcity and imbalance, inter-institutional data heterogeneity, etc., and discusses potential solutions and future research directions. This review aims to provide systematic guidance for researchers in the field of MRI and PET multimodal fusion, with the ultimate goal of advancing the development of early AD diagnosis and intervention strategies.