GenEyePose: Patient-Free, Knowledge-Based Saccadic Eye Movement Modeling for Digital Neurophysiologic Biomarker Development
2026-06-08 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created a way to generate completely fake eye movement data to help study quick eye movements called saccades, which can show brain problems. They used this fake data to train a computer program to tell if these eye movements were normal or showed certain issues. When tested on real patient data, the program worked fairly well, suggesting this fake data approach could help in clinics without needing real patient videos. This method could make eye movement tests more accessible and fast for spotting brain abnormalities.
saccadeseye movementsdigital biomarkersdeep learningsynthetic datavideo-oculographyneurologic diseasesAUROChypometriahypermetria
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
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.