A detection model of cognitive impairment via the integrated gait and eye movement analysis from a large Chinese community cohort

Abstract

AbstractINTRODUCTIONWhether the integration of eye‐tracking, gait, and corresponding dual‐task analysis can distinguish cognitive impairment (CI) patients from controls remains unclear.METHODSOne thousand four hundred eighty‐one participants, including 724 CI and 757 controls, were enrolled in this study. Eye movement and gait, combined with dual‐task patterns, were measured. The LightGBM machine learning models were constructed.RESULTSA total of 105 gait and eye‐tracking features were extracted. Forty‐six parameters, including 32 gait and 14 eye‐tracking features, showed significant differences between two groups (P < 0.05). Of these, the Gait_3Back‐TurnTime and Dual‐task cost‐TurnTime patterns were significantly correlated with plasma phosphorylated tau 181 (p‐tau181) level. A model based on dual‐task gait, dual‐task smooth pursuit, prosaccade, and anti‐saccade achieved the best area under the receiver operating characteristics curve (AUC) of 0.987 for CI detection, while combined with p‐tau181, the model discriminated mild cognitive impairment from controls with an AUC of 0.824.DISCUSSIONCombining dual‐task gait and dual‐task eye‐tracking analysis is feasible for the detection of CI.Highlights This is the first study to report the efficiency of integrated parameters of dual‐task gait and eye‐tracking for cognitive impairment (CI) detection in a large cohort. We identified 46 gait and eye‐tracking features associated with CI, and two were correlated to plasma phosphorylated tau 181. We constructed the model based on dual‐task gait, smooth pursuit, prosaccade, and anti‐saccade, achieving the best area under the curve of 0.987 for CI detection.