Green, K. E. | bioRxiv Science

bioRxiv2025

Privacy-Preserving Generative AI Framework for Synthetic Multimodal Eye Movement Data

Eye movements, such as nystagmus, saccades, and smooth pursuit, provide valuable information about neurological function but have limited publicly accessible datasets due to patient privacy concerns. To address this, we leverage generative AI to create realistic videos of artificial eye movement, eliminating the need for real patient data. These synthetic datasets have shown performance comparable to actual patient data in clinical tasks. Our generated videos will be openly shared, facilitating broader research and advancement in neurologic and neuro-ophthalmic AI applications.

bioengineering↗

bioRxiv2024

Deep Learning Detection of Subtle Torsional Eye Movements: Preliminary Results

The control of torsional eye position is a key component of ocular motor function. Ocular torsion can be affected by pathologies that involve ocular motor pathways, spanning from the vestibular labyrinth of the inner ears to various regions of the brainstem and cerebellum. Timely and accurate diagnosis enables efficient interventions and management of each case which are crucial for patients with dizziness, vertical double vision, or imbalance. Such detailed evaluation of eye movements may not be possible in all frontline clinical settings, particularly for detecting torsional abnormalities. These abnormalities are often more challenging to identify at the bedside compared to horizontal or vertical eye movements. To address these challenges, we used a dataset of torsional eye movements recorded with video-oculography (VOG) to develop deep learning models for detecting ocular torsion. Our models achieve 0.9308 AUROC and 86.79 % accuracy, leveraging ocular features particularly pertinent to tracking torsional eye position.

neuroscience↗