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

Rahman, A.; Bachina, P.; Patel, V.; Green, K. E.

doi:10.1101/2025.04.21.649786

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bioRxiv · 10.1101/2025.04.21.649786

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

Rahman, A.·Bachina, P.·Patel, V.·Green, K. E.

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Abstract

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.

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Rahman, A., Bachina, P., Patel, V., Green, K. E.. 2025-04-22. Privacy-Preserving Generative AI Framework for Synthetic Multimodal Eye Movement Data. https://doi.org/10.1101/2025.04.21.649786

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