GenEyePose: Patient-Free, Knowledge-Based Saccadic Eye Movement Modeling for Digital Neurophysiologic Biomarker Development

• URL: http://arxiv.org/abs/2606.09681v2
• Date: Tue, 09 Jun 2026 14:23:24 GMT
• Title: GenEyePose: Patient-Free, Knowledge-Based Saccadic Eye Movement Modeling for Digital Neurophysiologic Biomarker Development
• Abstract summary: Eye movements, including saccades, are widely regarded as highly sensitive and objective biomarkers of neurophysiologic states.<n>Currently, there are no robust AI-enabled video-oculographic solutions for screening, triaging, or localizing brain abnormalities due to privacy issues and scarce datasets.<n>We propose the first fully synthetic, patient-free, multimodal eye movement generation pipeline for generalizable saccade analysis.
• Score: 3.941624546506009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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