ArticleNPJ Parkinson's disease2025
Towards scalable screening for the early detection of Parkinson's disease: validation of an iPad-based eye movement assessment system against a clinical-grade eye tracker.
Article in NPJ Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Advanced Eye Movement Features Measured by Quantitative Oculography as Candidate Biomarkers for Progressive Supranuclear Palsy.Diagnostics (Basel, Switzerland) · 2026Review
- iPad Eye Tracking Reproduces Clinical Grade Oculomotor Differences in Parkinson's Disease.NPJ digital medicine · 2026Article
- A Feasibility Study of Tablet-Based Eye Movement Assessment Using a Built-In Camera: A Pilot Study.Journal of eye movement research · 2026Article
- Multimodal data-driven eye-movement subtypes and their cerebral glucose metabolic patterns in Parkinson's disease.Frontiers in aging neuroscience · 2026Article
- Innovative Sensor-Based Approaches for Assessing Neurodegenerative Diseases: A Brief State-of-the-Art Review.Sensors (Basel, Switzerland) · 2025Review
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Authors and funding
9 authors.
Funding
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Abstract
Early detection and monitoring of Parkinson's disease (PD) remain challenging, highlighting the need for accessible, cost-effective tools. Saccadic eye movement abnormalities are promising noninvasive biomarkers for PD screening and monitoring. Here, we present an iPad-based system that uses a deep learning algorithm to extract saccade metrics and validate these metrics against the clinical-grade EyeLink 1000 Plus. Twenty-five participants (10 with PD, 15 controls) completed pro-saccade, anti-saccade, memory-guided-saccade, and self-generated-saccade tasks. Relative to the EyeLink, the iPad system achieved average subject-level errors of 2 ms for latency and 0.7
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