Deep Learning-based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli

TL;DR

This paper introduces a deep learning approach using a multi-layered CNN to analyze eye-tracking heatmaps from 3D visual stimuli for diagnosing Alzheimer's Disease, offering a non-invasive and effective diagnostic tool.

Contribution

It proposes a novel multi-layered CNN model (MC-CNN) that effectively captures eye-movement behaviors for AD diagnosis using 3D visual stimuli and heatmaps.

Findings

MC-CNN achieves high accuracy in classifying AD and normal subjects.

The hierarchical convolution captures detailed eye-movement features.

The method demonstrates potential for non-invasive AD diagnosis.

Abstract

Alzheimer's Disease (AD) causes a continuous decline in memory, thinking, and judgment. Traditional diagnoses are usually based on clinical experience, which is limited by some realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as typical eye-tracking behavior, is of great clinical value to detect cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conduct a 3D comprehensive visual task on a non-invasive eye-tracking system to collect visual attention heatmaps. We then propose a multi-layered comparison convolution neural network (MC-CNN) to distinguish the visual attention differences between AD patients and normals. In MC-CNN, the multi-layered representations of heatmaps are obtained by hierarchical convolution to better encode eye-movement behaviors, which are further integrated into a distance vector to benefit the comprehensive visual task. Extensive experimental results on the collected dataset demonstrate that MC-CNN achieves consistent validity in classifying AD patients and normals with eye-tracking data.