Eye tracking and machine learning to assess cognitive impairment in post-COVID-19 patients

This study investigates whether oculomotor dysfunction in individuals with post-COVID-19 condition can serve as a biomarker for cognitive impairment as demonstrated in other neurological conditions. Eye movement metrics, including fixation, saccades, and smooth pursuit, as well as pupil size changes, were recorded using an eye tracker. Cognitive performance was assessed through established neuropsychological tests: Digit Symbol Test, Digit Span Backward, Trail Making Test (TMT), Stroop Color and Word Test (SCWT), together with the Controlled Oral Word Association Test (COWAT) for phonological fluency and an animal-naming task for semantic fluency. A total of 103 participants with post-COVID-19 condition were included in the analysis. Statistical correlations and k-means clustering were employed to analyse the relationship between oculomotor data and cognitive test results. Correlations between eye movements metrics and cognitive tests scores were modest ranging from 0.210 to 0.371. Clustering participants into three groups based on oculomotor metrics revealed statistically significant differences in cognitive performance. These findings suggest that eye tracking, unaffected by factors like language barriers and education level, my serve as a reliable method for assessing cognitive impairment in post-COVID-19 condition. This approach may complement neuropsychological tests, offering a more accessible and objective evaluation tool. Study registration: www.ClinicalTrials.gov, identifiers NCT05307575 (01-10-2021) and NCT05846126 (01-05-2023).