INTRODUCTIONOculomotor and gait dysfunctions are closely associated with cognition. However, oculo‐gait patterns and their correlation with cognition in cerebral small vessel disease (CSVD) remain unclear.METHODSPatients with CSVD from a hospital‐based cohort (n = 194) and individuals with presumed early CSVD from a community‐based cohort (n = 319) were included. Oculo‐gait patterns were measured using the artificial intelligence (AI) –assisted ‘EyeKnow’ eye‐tracking and ‘ReadyGo’ motor evaluation systems. Multivariable linear and logistic regression models were employed to investigate the association between the oculo‐gait parameters and cognition.RESULTSAnti‐saccade accuracy, stride velocity, and swing velocity were significantly associated with cognition in both patients and community dwellers with CSVD, and could identify cognitive impairment in CSVD with moderate accuracy (area under the curve [AUC]: hospital cohort, 0.787; community cohort, 0.810) after adjusting for age and education.DISCUSSIONThe evaluation of oculo‐gait features (anti‐saccade accuracy, stride velocity, and swing velocity) may help screen cognitive impairment in CSVD.Highlights Oculo‐gait features (lower anti‐saccade accuracy, stride velocity, and swing velocity) were associated with cognitive impairment in cerebral small vessel disease (CSVD). Logistic model integrating the oculo‐gait features, age, and education level moderately distinguished cognitive status in CSVD. Artificial intelligence–assisted oculomotor and gait measurements provide quick and accurate evaluation in hospital and community settings.

中文翻译：

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人工智能辅助眼球步态测量对脑小血管病认知障碍的分析


引言动眼神经和步态功能障碍与认知密切相关。然而，在脑小血管病 （CSVD） 中，眼球步态模式及其与认知的相关性仍不清楚。方法包括来自医院队列的 CSVD 患者 （n = 194） 和来自社区队列的推定早期 CSVD 的个体 （n = 319）。使用人工智能 （AI） 辅助的“EyeKnow”眼动追踪和“ReadyGo”运动评估系统测量眼球步态模式。采用多变量线性和 logistic 回归模型来研究眼步态参数与认知之间的关联。结果眼跳准确性、步幅速度和摆动速度与 CSVD 患者和社区居民的认知显著相关，并且可以在调整年龄和教育程度后以中等准确性识别 CSVD 中的认知障碍 （曲线下面积 [AUC]：医院队列，0.787;社区队列，0.810）。讨论对眼步态特征（抗扫视准确性、步幅速度和摆动速度）的评估可能有助于筛查 CSVD 中的认知障碍。亮点眼球步态特征 （较低的抗扫视准确性、步幅速度和摆动速度） 与脑小血管病 （CSVD） 的认知障碍相关。整合眼球步态特征、年龄和教育水平的 Logistic 模型中度区分了 CSVD 的认知状态。人工智能辅助的动眼运动和步态测量可在医院和社区环境中提供快速准确的评估。