Alzheimer's disease (AD) poses a growing global health challenge, with visuospatial impairments emerging as early indicators that can be detected through eye movement analysis. However, existing methods face two key limitations: first, they often analyze eye movements in isolation, without exploiting explicit saliency patterns for comparison; second, temporal attentional dynamics across sequences remain underexplored and fail to capture the temporal evolution of abnormal visuospatial patterns. To address these challenges, we propose a depth-induced saliency comparison network (DISCN). The DISCN first employs a depth-included salient attention module (DSAM) to construct comprehensive objective-subjective saliency priors from the saliency of RGB-D visual stimuli and normal control eye movements. A saliency-aware serial attention module (SSAM) then applies temporal attention to eye movements elicited by sequential stimuli to characterize dynamic visuospatial abnormalities. Experimental results on both internal and external cohorts demonstrate that the DISCN achieves robust performance in terms of distinguishing AD patients from normal controls by using eye movements.
Liu et al. (Thu,) studied this question.