用眼动数据自动筛查帕金森病，准确率高达95%。
Automatic Screening of Parkinson's Disease from Visual Explorations
- 结合经典眼动特征与注视聚类区域，提取六种视觉任务中的眼动指标
- 集成模型在独立测试集上达到0.95的AUC，优于单个分类器
- 适合临床早期筛查，无需侵入性检测，可推广至智能健康设备
眼动可揭示神经退行性病变的早期迹象，包括帕金森病（PD）。本文研究了一组基于注视的眼动特征在不同视觉探索任务中自动筛查PD的可行性。提出一种新方法，融合经典固定/扫视眼动特征（如扫视次数、固定时长、扫描范围）与由注视聚集区域导出的特征。这些特征从六个探索性测试中自动提取，并通过多种机器学习分类器进行评估。采用专家混合集成模型整合各测试及双眼的输出结果。实验显示，集成模型性能优于单一分类器，在保留测试集上实现0.95的受试者工作特征曲线下面积（AUC）。结果表明，视觉探索是一种具有前景的无创早期帕金森病自动筛查工具。
原文摘要 · Abstract (English)
Eye movements can reveal early signs of neurodegeneration, including those associated with Parkinson's Disease (PD). This work investigates the utility of a set of gaze-based features for the automatic screening of PD from different visual exploration tasks. For this purpose, a novel methodology is introduced, combining classic fixation/saccade oculomotor features (e.g., saccade count, fixation duration, scanned area) with features derived from gaze clusters (i.e., regions with a considerable accumulation of fixations). These features are automatically extracted from six exploration tests and evaluated using different machine learning classifiers. A Mixture of Experts ensemble is used to integrate outputs across tests and both eyes. Results show that ensemble models outperform individual classifiers, achieving an Area Under the Receiving Operating Characteristic Curve (AUC) of 0.95 on a held-out test set. The findings support visual exploration as a non-invasive tool for early automatic screening of PD.
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