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眼动变化作为轻度认知障碍的一个指标

Julius Opwonya^1,2, Boncho Ku¹, Kun Ho Lee^3,4,5

¹Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea.

Frontiers in neuroscience

|July 3, 2023

概括

预测轻度认知障碍 (MCI) 是可能使用非侵入性眼动 (EM) 度量. 将EM数据与人口统计和认知得分相结合,为早期发现认知衰退提供了一种具有成本效益的方法.

科学领域:

神经科学是一个神经科学.
眼科医生眼科眼科
老年学是一门学科.

背景情况:

早期发现痴呆症对于预防疾病进展至关重要.
目前的诊断工具昂贵且耗时.
对于早期认知衰退检测,需要使用非侵入性,具有成本效益的方法.

研究的目的:

开发非侵入性和具有成本效益的分类模型,用于预测轻度认知障碍 (MCI).
为了利用眼动 (EM) 数据进行MCI预测.

主要方法:

收集了594名参与者的眼睛跟踪 (ET) 数据 (428名对照,166名MCI).
使用后勤回归 (LR) 来分析EM指标和赔率比率 (OR).
采用了机器学习模型与EM指标,人口统计和认知分数,由AUROC评估.

主要成果:

LR模型显示EM指标与MCI赔率增加显著相关 (ORs 1.213-1.621).
将人口统计与EM或MMSE相结合的模型实现了0.752和0.767.7的AUROC.
最好的模型整合了人口统计,MMSE和EM指标,达到0.840.8的AUROC.

结论:

在MCI中,EM指标的变化与注意力和执行功能缺陷相关.
将EM指标与人口统计和认知得分相结合,可以改善MCI预测.
这种方法提供了一种非侵入性的,具有成本效益的方法来识别早期的认知衰退.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.眼球运动分析和合成机器学习 (ML) 是指机器学习.轻度的认知障碍轻度的认知障碍萨卡德斯 (saccades) 是一个古老的书.

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