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

Source: arXiv - 2606.09681v1

Overview

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.

Key Contributions

This paper presents research in the following areas:

• cs.CV

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.CV.

Authors

• Tianyu Lin
• Jooyoung Ryu
• Puvada Sreevarsha
• Rahul Srinivasaragavan
• Riya Satavlekar
• Susan Kim
• Nidhi Soley
• Yujie Yan
• Ishan Vatsaraj
• Carl Harris
• Aimon Rahman
• Vishal Patel
• Joseph Greenstein
• Casey Taylor
• Kemar E. Green

Paper Information

• arXiv ID: 2606.09681v1
• Categories: cs.CV
• Published: June 8, 2026
• PDF: Download PDF