Analysis of eye movement features in patients with Alzheimer's disease based on intelligent eye movement analysis and evaluation system.

Background: The early identification of Alzheimer's disease (AD) benefits patients, so creating a simple and convenient method is crucial for diagnosing early symptoms.

Objective: To offer a potential approach for the early detection of both AD and mild cognitive impairment (MCI).

Methods: Eye movement data from 66 patients were divided into three groups, including healthy control group (HC), MCI group, and AD group. We searched for parameters that can detect MCI at an early stage and drew receiver operating characteristic (ROC) curves. The correlation between eye movement parameters and cognitive scores was analyzed.

Results: The MCI group differed from the HC group in error correction rate of antisaccade ( = 0.008) and total offset degrees (>4°) ( = 0.011) of lateral fixation. The AD group had different overlap prosaccade accuracy ( = 0.025), latency ( = 0.009) and average completion time ( = 0.015), gap prosaccade latency ( = 0.005) and average completion time ( = 0.005), antisaccade accuracy ( = 0.006), error correction rate (  0.001) and average saccade velocity ( = 0.035), and lateral fixation accuracy ( = 0.018), total offset degrees (>4°) ( = 0.041) compared to the HC group. The AD group differed significantly from the MCI group in accuracy ( = 0.001) and error correction rate ( = 0.044) of antisaccades, the latency ( = 0.009) and average completion time ( = 0.025) of overlap prosaccade and the latency ( = 0.038) of gap prosaccade, these parameters can serve as indicators to monitor the progress of the disease. Lateral fixation combined with antisaccade was more conducive to identifying MCI patients with the area under the ROC curve of 0.837. Most eye movement parameters had a light to moderate correlation with cognitive scores.

Conclusions: Eye movements can be used for early identification of MCI/AD patients and to monitor disease progression.