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Communication Dans Un Congrès Année : 2024

Deep learning method for accessible eccentric photorefraction

Résumé

Affordable and accessible screening tools are crucial to diagnose and, hence, effectively address vision impairment. We propose a novel pipeline based on photorefraction which complies with these requirements. It includes an innovative, easy-to-use, optical portable device combined with an image processing algorithm to automatically compute refraction errors from twelve jointly captured images. To do so, a supervised deep convolutional model is proposed, trained on synthetic images generated by a physical-aware simulator, as well as on real images carefully collected from a cohort of volunteers. We provide in this article an assessment of our approach accuracy and robustness on both simulated and real datasets. Our promising results pave the way for a practical application of the proposed device for an accurate vision loss diagnostic in a real setting.
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hal-04488060 , version 1 (04-03-2024)

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  • HAL Id : hal-04488060 , version 1

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Mathieu Vu, Emilie Chouzenoux, Jean-Christophe Pesquet, Stéphane Boutinon, Marius Peloux, et al.. Deep learning method for accessible eccentric photorefraction. ISBI 2024 - 21st IEEE International Symposium on Biomedical Imaging, May 2024, Athènes, Greece. ⟨hal-04488060⟩
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