眼跳和收敛特性能区分脑卒中幸存者和其他病理个体吗?机器学习方法。

IF 2.7 3区 医学 Q3 NEUROSCIENCES
Alae Eddine El Hmimdi, Zoï Kapoula
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引用次数: 0

摘要

最近将机器学习(ML)应用于扫视和收敛眼运动的研究表明,机器学习能够将患有阅读障碍、学习障碍或注意力障碍的个体与健康个体或其他病理个体区分开来。中风患者通常表现为视觉缺陷和眼球运动障碍。本研究的重点是使用REMOBI技术V3和瞳孔核心眼动仪进行扫视和会聚测量。眼动数据采用Orasis-Ear开发的AIDEAL V3(人工智能眼动分析)云软件自动分析。该软件为每种类型的眼球运动计算多个参数,包括延迟、准确性、速度、持续时间和分离。将三种ML模型(逻辑回归、支持向量机、随机森林)应用于AIDEAL提供的扫视和收敛眼动特征,以从其他组中识别脑卒中患者:有学习障碍的儿童群体和有更广泛功能障碍或病理的人群(包括儿童和成人)。不同分类器基于眼跳和收敛参数识别脑卒中患者的宏观F1得分高达75.9%。另一项使用年龄匹配组的脑卒中患者和成人或老年人的ML分析减少了年龄差异的影响。该分析结果显示,在所有三种ML模型中,F1得分更高,因为对照组主要包括健康个体,包括一些患有老年性痴呆的个体。综上所述,通过REMOBI和AIDEAL技术测量的扫视和会聚眼动参数的ML应用是一种检测脑卒中相关后遗症的灵敏方法。该方法可以进一步发展为评估脑卒中患者神经功能缺损的恢复、代偿和演变的临床工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can Saccade and Vergence Properties Discriminate Stroke Survivors from Individuals with Other Pathologies? A Machine Learning Approach.

Recent studies applying machine learning (ML) to saccade and vergence eye movements have demonstrated the ability to distinguish individuals with dyslexia, learning disorders, or attention disorders from healthy individuals or those with other pathologies. Stroke patients are known to exhibit visual deficits and eye movement disorders. This study focused on saccade and vergence measurements using REMOBI technology V3 and the Pupil Core eye tracker. Eye movement data were automatically analyzed with the AIDEAL V3 (Artificial Intelligence Eye Movement Analysis) cloud software developed by Orasis-Ear. This software computes multiple parameters for each type of eye movement, including the latency, accuracy, velocity, duration, and disconjugacy. Three ML models (logistic regression, support vector machine, random forest) were applied to the saccade and vergence eye movement features provided by AIDEAL to identify stroke patients from other groups: a population of children with learning disorders and a population with a broader spectrum of dysfunctions or pathologies (including children and adults). The different classifiers achieved macro F1 scores of up to 75.9% in identifying stroke patients based on the saccade and vergence parameters. An additional ML analysis using age-matched groups of stroke patients and adults or seniors reduced the influence of large age differences. This analysis resulted in even higher F1 scores across all three ML models, as the comparison group predominantly included healthy individuals, including some with presbycusis. In conclusion, ML applied to saccade and vergence eye movement parameters, as measured by the REMOBI and AIDEAL technology, is a sensitive method for the detection of stroke-related sequelae. This approach could be further developed as a clinical tool to evaluate recovery, compensation, and the evolution of neurological deficits in stroke patients.

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来源期刊
Brain Sciences
Brain Sciences Neuroscience-General Neuroscience
CiteScore
4.80
自引率
9.10%
发文量
1472
审稿时长
18.71 days
期刊介绍: Brain Sciences (ISSN 2076-3425) is a peer-reviewed scientific journal that publishes original articles, critical reviews, research notes and short communications in the areas of cognitive neuroscience, developmental neuroscience, molecular and cellular neuroscience, neural engineering, neuroimaging, neurolinguistics, neuropathy, systems neuroscience, and theoretical and computational neuroscience. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files or software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
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