Clinical parkinsonism & related disorders
Automated eye-tracking for Parkinson's disease diagnosis: a proof-of-concept cascade classifier study
Door Menke JM, Aslam S, Rieiro H en 2 anderen.
Inhoud in het Engels
Abstract
Background
Parkinson's disease (PD) is a progressive neurodegenerative disorder of increasing prevalence, with diagnostic accuracy of approximately 26% in early symptomatic patients. There is a need for accurate, non-invasive biomarkers to aid in disease diagnosis.
Methods
This proof-of-concept study enrolled 90 participants (PD n = 30, other movement disorders [OM] n = 30, healthy controls [HC] n = 30) at a single institution. Participants completed two 10-min eye- tracking sessions using the SaccadeDX™ 250-Hz binocular system. A two-level cascade classifier was fitted using elastic-net feature selection followed by logistic regression on the selected features, validated by 10-fold cross-validation. The cascade distinguished HC from movement disorders (Level 1) and PD from OM (Level 2), with the objective of establishing clinical validity - that an eye-tracking signal correlates reliably with PD diagnosis.