Eye movements, including saccades, are widely regarded as highly sensitive and objective biomarkers of neurophysiologic states. Detecting saccadic signatures in neurologic diseases offers a rapid, portable alternative to brain imaging, avoiding access and cost barriers. Currently, there are no robust AI-enabled video-oculographic solutions (e.g., digital biomarkers) for screening, triaging, or localizing brain abnormalities due to privacy issues and scarce datasets. In this work, we propose the first fully synthetic, patient-free, multimodal eye movement generation pipeline for generalizable saccade analysis. Using this synthetic dataset, we trained a deep learning classifier to distinguish between normal and abnormal (hypometria and hypermetria) saccadic accuracies and evaluated its performance on real-world clinical data. The model achieved an AUROC of 0.76 and a sensitivity of 0.71, showing that the synthetic data has strong potential to generalize for clinical applications, including as a screening tool in at-home and emergency room settings or a tool for precise neuroanatomic localization.

翻译：眼球运动（包括扫视）被广泛认为是神经生理状态的高度敏感且客观的生物标志物。在神经系统疾病中检测扫视特征可提供一种快速、便携的替代脑部成像方案，从而规避可及性与成本障碍。目前，由于隐私问题及数据集稀缺，尚无稳健的基于人工智能的视频眼动记录解决方案（如数字生物标志物）可用于筛查、分诊或定位脑部异常。本研究首次提出全合成、无患者的扫视分析通用多模态眼球运动生成流程。利用该合成数据集，我们训练了一个深度学习分类器以区分正常与异常（低幅扫视与高幅扫视）扫视准确度，并在真实临床数据上评估其性能。该模型实现了0.76的AUROC与0.71的灵敏度，表明合成数据在临床应用中具备强大的泛化潜力，可作为居家及急诊环境中的筛查工具或精确神经解剖定位工具。