Objective: Accurate quantification of neurodegenerative disease progression is an ongoing challenge that complicates efforts to understand and treat these conditions. Clinical studies have shown that eye movement features may serve as objective biomarkers to support diagnosis and tracking of disease progression. Here, we demonstrate that saccade latency—an eye movement measure of reaction time—can be measured robustly outside of the clinical environment with a smartphone camera. Methods: To enable tracking of saccade latency in large cohorts of patients and control subjects, we combined a deep convolutional neural network for gaze estimation with a model-based approach for saccade onset determination that provides automated signal-quality quantification and artifact rejection. Results: Simultaneous recordings with a smartphone and a high-speed camera resulted in negligible differences in saccade latency distributions. Furthermore, we demonstrated that the constraint of chinrest support can be removed when recording healthy subjects. Repeat smartphone-based measurements of saccade latency in 11 self-reported healthy subjects resulted in an intraclass correlation coefficient of 0.76, showing our approach has good to excellent test–retest reliability. Additionally, we conducted more than 19 000 saccade latency measurements in 29 self-reported healthy subjects and observed significant intra- and inter-subject variability, which highlights the importance of individualized tracking. Lastly, we showed that with around 65 measurements we can estimate mean saccade latency to within less-than-10-ms precision, which takes within 4 min with our setup. Conclusion and Significance: By enabling repeat measurements of saccade latency and its distribution in individual subjects, our framework opens the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.

中文翻译：

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使用智能手机摄像头测量扫视延迟

目的：准确量化神经退行性疾病的进展是一项持续不断的挑战，使了解和治疗这些疾病的努力变得复杂。临床研究表明，眼球运动特征可以用作客观的生物标志物，以支持诊断和跟踪疾病的进展。在这里，我们证明了扫视潜伏期（一种反应时间的眼动指标）可以在智能手机摄像头以外的临床环境中进行稳健的测量。方法：为了能够追踪大批患者和对照受试者的扫视潜伏期，我们将深度卷积神经网络用于凝视估计，并结合了基于模型的扫视发作确定方法，该方法可提供自动信号质量量化和伪像抑制。结果：使用智能手机和高速摄像头进行的同时录制导致扫视潜伏期分布的差异可忽略不计。此外，我们证明了记录健康受试者时可以消除对下颌支撑的限制。重复对11位自我报告的健康受试者进行扫视潜伏期的基于智能手机的测量，得出的组内相关系数为0.76，这表明我们的方法具有出色的重测信度。此外，我们在29位自我报告的健康受试者中进行了超过1.9万次扫视潜伏期测量，并观察到受试者之间和受试者之间的显着差异，这突出了个性化追踪的重要性。最后，我们表明，通过大约65次测量，我们可以将平均扫视延迟估计在不到10毫秒的精度内，我们的设置需要4分钟的时间。结论和意义：通过对扫视潜伏期及其在各个受试者中的分布进行重复测量，我们的框架为在较广范围的人群中以更短的时间尺度量化患者状态提供了可能性。