Development and validation of a pixel wise deep learning model to detect cataract on swept-source optical coherence tomography images.

Journal: Journal of optometry
Published Date: Jan 1, 2022

Abstract

PURPOSE: The diagnosis of cataract is mostly clinical and there is a lack of objective and specific tool to detect and grade it automatically. The goal of this study was to develop and validate a deep learning model to detect and localize cataract on Swept Source Optical Coherance Tomography (SS-OCT) images.

Authors

  • Pierre Zéboulon
    Department of Ophthalmology, Rothschild Foundation, Paris, France. Electronic address: pierrezeboulon@gmail.com.
  • Christophe Panthier
    Department of Ophthalmology, Rothschild Foundation, 25 Rue Manin, Paris 75019, France.
  • Hélène Rouger
    Department of Ophthalmology, Rothschild Foundation, 25 Rue Manin, Paris 75019, France.
  • Jacques Bijon
    Department of Ophthalmology, Rothschild Foundation, 25 Rue Manin, Paris 75019, France.
  • Wassim Ghazal
    Department of Ophthalmology, Rothschild Foundation, Paris, France ; and.
  • Damien Gatinel
    Department of Ophthalmology, Rothschild Foundation, Paris, France; CEROC (Center of Expertise and Research in Optics for Clinicians), Paris, France.

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