Abstract
Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Reality technology for the evaluation of neurodegenerative diseases. Additionally, we introduce a pipeline for extracting clinically relevant features from eye-gaze analysis, describing the capabilities of the proposed system from a medical perspective. The study involved a cohort of healthy control individuals and patients suffering from Parkinson's disease, showcasing the feasibility and potential of the proposed technology for non-intrusive monitoring of eye movement patterns for the diagnosis of neurodegenerative diseases. Clinical relevance - Developing a non-invasive biomarker for Parkinson's disease is urgently needed to accurately detect the disease's onset. This would allow for the timely introduction of neuroprotective treatment at the earliest stage and enable the continuous monitoring of intervention outcomes. The ability to detect subtle changes in eye movements allows for early diagnosis, offering a critical window for intervention before more pronounced symptoms emerge. Eye tracking provides objective and quantifiable biomarkers, ensuring reliable assessments of disease progression and cognitive function. The eye gaze analysis using Mixed Reality glasses is wireless, facilitating convenient assessments in both home and hospital settings. The approach offers the advantage of utilizing hardware that requires no additional specialized attachments, enabling examinations through personal eyewear.
Abstract (translated)
Parkinson's disease is ranked as the second most prevalent neurodegenerative disorder globally.这项研究旨在开发一个利用混合现实技术跟踪和评估眼动功能的系统。在本文中，我们提出了一个医学场景，概述了开发旨在评估神经退行性疾病眼动功能的应用程序。此外，我们还介绍了从眼动分析中提取与临床相关的特征的途径，从医学角度描述了所提议系统的能力。该研究涉及了一组健康对照者和患有帕金森病的患者，展示了所提出技术非侵入性监测眼动模式对神经退行性疾病诊断的潜力和可行性。临床意义 - 迫切需要为帕金森病开发一种非侵入性的生物标志物，以准确检测疾病的发生。这将使得在疾病最早期引入神经保护治疗，并能够连续监测治疗效果。能够检测眼动模式的变化，使得早期诊断成为可能，为干预提供关键窗口。眼跟踪提供了客观和量化的生物标志物，确保了疾病进展和认知功能的有据可依的评估。使用混合现实眼镜进行眼动分析是无偿的、方便的，在家庭和医院环境中都提供了便利的评估。这种方法利用了无需额外专业附件的硬件，通过个人眼镜进行检查。
URL
https://arxiv.org/abs/2404.12984
