Artificial intelligence–assisted oculo-gait measurements for cognitive impairment in cerebral small vessel disease

IF 13 1区医学 Q1 CLINICAL NEUROLOGY

Alzheimer's & Dementia Pub Date : 2024-10-16 DOI:10.1002/alz.14288

Huimin Chen, Hao Du, Fang Yi, Tingting Wang, Shuo Yang, Yuesong Pan, Hongyi Yan, Dandan Liu, Mengyuan Zhou, Yiyi Chen, Mengxi Zhao, Jingtao Pi, Yingying Yang, Xiangmin Fan, Xueli Cai, Ziyu Qiu, Jipeng Zhang, Yawei Liu, Wenping Gu, Yilong Wang

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引用次数: 0

Abstract

INTRODUCTION

Oculomotor 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.

METHODS

Patients 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.

RESULTS

Anti-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.

DISCUSSION

The 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.

Abstract Image

查看原文本刊更多论文

人工智能辅助眼视镜测量脑小血管疾病的认知障碍。

导言眼球运动和步态功能障碍与认知能力密切相关。方法纳入医院队列中的 CSVD 患者（194 人）和社区队列中的推测早期 CSVD 患者（319 人）。使用人工智能（AI）辅助的 "EyeKnow "眼动跟踪系统和 "ReadyGo "运动评估系统测量眼动步态。结果反踱步准确度、步速和摆动速度与 CSVD 患者和社区居民的认知能力显著相关，并能以中等准确度识别 CSVD 患者的认知障碍（曲线下面积 [AUC]：医院队列，0.摘要眼步态特征（较低的反斜视精确度、步速和摆动速度）与脑小血管病（CSVD）的认知功能障碍有关。整合了眼动步态特征、年龄和教育水平的逻辑模型可适度区分脑小血管病患者的认知状况。人工智能辅助眼球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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer's & Dementia 医学-临床神经学

CiteScore

14.50

自引率

5.00%

发文量

299

审稿时长

3 months

期刊介绍： Alzheimer's & Dementia is a peer-reviewed journal that aims to bridge knowledge gaps in dementia research by covering the entire spectrum, from basic science to clinical trials to social and behavioral investigations. It provides a platform for rapid communication of new findings and ideas, optimal translation of research into practical applications, increasing knowledge across diverse disciplines for early detection, diagnosis, and intervention, and identifying promising new research directions. In July 2008, Alzheimer's & Dementia was accepted for indexing by MEDLINE, recognizing its scientific merit and contribution to Alzheimer's research.