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
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 use...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | npj Parkinson's Disease |
| Online Access: | https://doi.org/10.1038/s41531-025-01079-9 |
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| Summary: | 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∘ for amplitude in pro-, anti-, and memory-guided saccades, and 0.003 s−1 for inter-saccadic rate and 1.6∘ for amplitude in self-generated saccades. A review of 22 studies on PD-related saccadic impairments established benchmarks for clinically meaningful effects. The iPad-based system meets or exceeds these benchmarks, supporting its use as a scalable and cost-effective tool for screening and monitoring PD. |
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| ISSN: | 2373-8057 |