  Alzheimer's disease is a progressive neurological disorder characterized by
cognitive impairment and memory loss. With the increasing aging population, the
incidence of AD is continuously rising, making early diagnosis and intervention
an urgent need. In recent years, a considerable number of teams have applied
computer-aided diagnostic techniques to early classification research of AD.
Most studies have utilized imaging modalities such as magnetic resonance
imaging (MRI), positron emission tomography (PET), and electroencephalogram
(EEG). However, there have also been studies that attempted to use other
modalities as input features for the models, such as sound, posture,
biomarkers, cognitive assessment scores, and their fusion. Experimental results
have shown that the combination of multiple modalities often leads to better
performance compared to a single modality. Therefore, this paper will focus on
different modalities and their fusion, thoroughly elucidate the mechanisms of
various modalities, explore which methods should be combined to better harness
their utility, analyze and summarize the literature in the field of early
classification of AD in recent years, in order to explore more possibilities of
modality combinations.
