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<p>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<sup>∘</sup> for amplitude in pro-, anti-, and memory-guided saccades, and 0.003 s<sup>−1</sup> for inter-saccadic rate and 1.6<sup>∘</sup> 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.</p>

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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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月8日

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  • Jamie Koerner,
  • Erin Zou,
  • Jessica A. Karl,
  • Cynthia Poon,
  • Leo Verhagen Metman,
  • Charles G. Sodini,
  • Vivienne Sze,
  • Fabian J. David,
  • Thomas Heldt

摘要

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.