Evaluation of Stereopsis Performance, Gaze Direction and Pupil Diameter in Post-COVID Syndrome Using Machine Learning

TL;DR

This study uses machine learning to assess how well virtual reality eye-tracking features can distinguish people with post-COVID syndrome from healthy individuals.

Contribution

The study identifies that pupil diameter features, particularly the index of pupillary activity, are most effective in distinguishing post-COVID syndrome patients.

Findings

Pupil diameter features achieved the highest AUROC of 0.73 in distinguishing PCS patients from healthy controls.

The index of pupillary activity (IPA) was the most predictive single feature.

More difficult tests within the VR-OTS system showed higher predictive performance.

Abstract

Background/Objectives: Post-COVID syndrome (PCS) encompasses symptoms that persist for at least 12 weeks after the onset of a COVID-19 infection and cannot be explained by other causes. The most common symptoms are fatigue, cognitive impairments, and physical limitations. The objective diagnosis of PCS is still challenging, as specific biomarkers are lacking. One possibility to measure cognitive impairment is the virtual-reality-oculomotor-test-system (VR-OTS, Talkingeyes & More, Germany). It shows stereoscopic stimuli in a VR-environment to the test person. While working on the visual tasks, many features are recorded. These features can be categorized into three groups: stereopsis performance, gaze direction, and pupil diameter. The aim of this study was to investigate which of these three feature groups is best to distinguish patients with PCS from a healthy control group. Methods: In total, 429 patients with PCS were recruited within the disCOVer 1.0 and disCOVer 2.0 study at the Department of Ophthalmology, Universitätsklinikum (Erlangen, Germany). All patients received VR-OTS measurements. From these measurements, a total of 95 features were extracted, which can be categorized into three groups: gaze direction, pupil diameter, and stereopsis performance. In the first step, support vector machines (SVMs) were trained on these different feature sets and evaluated using the area under receiver operating characteristic (AUROC) as the evaluation metric. In the second step, the same procedure was repeated with each feature independently to investigate which were most the predictive per group. Results: The SVM using the pupil diameter features yielded an AUROC of 0.73, the one using the gaze direction features resulted in an AUROC of 0.68. and the stereopsis performance features produced an AUROC of 0.66. The SVM using all VR-OTS data showed an AUROC of 0.68. For the single features, the index of pupillary activity (IPA) showed the best discrimination. Moreover, all features that were evaluated at different difficulties showed the same pattern—that the more difficult test proved to be more predictive. Conclusions: The study showed that VR-OTS can distinguish between patients with PCS and healthy control probands. Since different features showed a better performance than others, it makes sense for further studies to use a subset of the available features for further analysis.

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