用合成数据训练眼动模型，无须真实患者即可筛查脑部异常
GenEyePose: Patient-Free, Knowledge-Based Saccadic Eye Movement Modeling for Digital Neurophysiologic Biomarker Development
- 全合成眼动数据生成，无需真实患者隐私数据
- 模型在真实临床数据上达AUROC 0.76、灵敏度0.71
- 适合居家或急诊筛查，可定位神经病灶位置
眼动，包括扫视运动，被认为是神经生理状态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，提供了一种快速、便携的替代脑成像的方法，避免了访问和成本障碍。目前，由于隐私问题和数据集稀缺，尚无可靠的基于AI的视频眼动分析解决方案（如数字生物标志物）用于筛查、分诊或定位脑部异常。本文提出首个完全合成、无需患者、多模态的眼动生成管道，实现可泛化的扫视分析。利用该合成数据集，训练深度学习分类器以区分正常与异常（低幅和高幅）扫视准确性，并在真实临床数据上评估其性能。模型在真实数据上取得AUROC 0.76和灵敏度0.71，表明合成数据具有强泛化潜力，可用于临床应用，包括家庭筛查或急诊室使用，或作为精确定位神经解剖位置的工具。
原文摘要 · Abstract (English)
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.
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