Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases

TL;DR

This study develops a Mixed Reality-based eye-tracking system to non-invasively diagnose and monitor neurodegenerative diseases like Parkinson's, demonstrating its feasibility and potential for early detection and ongoing assessment.

Contribution

The paper introduces a novel Mixed Reality application for eye-tracking that captures clinically relevant features for neurodegenerative disease diagnosis, with validation on patient and control groups.

Findings

Feasibility of using Mixed Reality glasses for eye-tracking in clinical settings

Ability to distinguish Parkinson's patients from healthy controls based on eye movement patterns

Potential for early diagnosis and continuous monitoring of neurodegenerative diseases

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.