Deep Learning-Based Assisted Diagnosis of Alzheimer's Disease: From Monomodal Imaging to Multimodal Fusion

Abstract

In the field of auxiliary diagnosis of Alzheimer's disease (AD), deep learning is developing along two paths: single-modal imaging and multi-modal fusion. The single-modal approach utilizes lightweight networks (with efficient receptive fields and multi-axis attention mechanisms) to optimize preprocessing and improve three-dimensional models, achieving high-precision classification while controlling the number of parameters; multi-modal fusion integrates structural magnetic resonance, genetic information, scale data, diffusion tensor imaging, and electroencephalogram, and employs technical means such as Transformer and graph convolutional networks to significantly enhance the performance of early identification and multi-classification. The existing limitations include weak cross-center generalization ability, difficulties in obtaining and aligning multi-modal data, insufficient interpretability, and the lack of longitudinal prediction. Future development directions include lightweight edge deployment, federated and self-supervised learning, incorporating low-cost modalities such as plasma markers, and constructing longitudinal prediction and frameworks with pathological prior interpretability, thereby facilitating the implementation of clinical applications. This article aims to provide a reference for the further optimization and clinical translation of related methods by summarizing the main technical paths, application progress and existing problems of deep learning in the auxiliary diagnosis of Alzheimer's disease.