Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052, Erlangen, Bavaria, Germany.
Department of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany.
Sci Rep. 2023 Aug 13;13(1):13167. doi: 10.1038/s41598-023-40263-w.
In 2019, we faced a pandemic due to the coronavirus disease (COVID-19), with millions of confirmed cases and reported deaths. Even in recovered patients, symptoms can be persistent over weeks, termed Post-COVID. In addition to common symptoms of fatigue, muscle weakness, and cognitive impairments, visual impairments have been reported. Automatic classification of COVID and Post-COVID is researched based on blood samples and radiation-based procedures, among others. However, a symptom-oriented assessment for visual impairments is still missing. Thus, we propose a Virtual Reality environment in which stereoscopic stimuli are displayed to test the patient's stereopsis performance. While performing the visual tasks, the eyes' gaze and pupil diameter are recorded. We collected data from 15 controls and 20 Post-COVID patients in a study. Therefrom, we extracted features of three main data groups, stereopsis performance, pupil diameter, and gaze behavior, and trained various classifiers. The Random Forest classifier achieved the best result with 71% accuracy. The recorded data support the classification result showing worse stereopsis performance and eye movement alterations in Post-COVID. There are limitations in the study design, comprising a small sample size and the use of an eye tracking system.
2019 年，我们面临着由冠状病毒病（COVID-19）引起的大流行，有数百万人被确诊，并报告了死亡病例。即使是在已康复的患者中，症状也可能持续数周，被称为“后 COVID 症状”。除了疲劳、肌肉无力和认知障碍等常见症状外，还报告了视力障碍。目前，基于血液样本和基于辐射的程序等方法对 COVID 和后 COVID 进行自动分类的研究正在进行中。然而，对于视力障碍的基于症状的评估仍然缺失。因此，我们提出了一个虚拟现实环境，在该环境中可以显示立体刺激，以测试患者的立体视觉表现。在执行视觉任务时，记录眼睛的注视和瞳孔直径。我们在一项研究中收集了 15 名对照者和 20 名后 COVID 患者的数据。从中，我们提取了立体视觉表现、瞳孔直径和注视行为三个主要数据组的特征，并训练了各种分类器。随机森林分类器的准确率最高，达到了 71%。记录的数据支持分类结果，表明后 COVID 患者的立体视觉表现更差，眼球运动改变。该研究设计存在局限性，包括样本量小和使用眼动追踪系统。
