Deep Learning Model for the Detection of Corneal Edema Before Descemet Membrane Endothelial Keratoplasty on Optical Coherence Tomography Images.

Journal: Translational vision science & technology
PMID: 36583911

Abstract

PURPOSE: Descemet membrane endothelial keratoplasty (DMEK) is the preferred method for treating corneal endothelial dysfunction, such as Fuchs endothelial corneal dystrophy (FECD). The surgical indication is based on the patients' symptoms and the presence of corneal edema. We developed an automated tool based on deep learning to detect edema in corneal optical coherence tomography images. This study aimed to evaluate this approach in edema detection before Descemet membrane endothelial keratoplasty surgery, for patients with or without FECD.

Authors

  • Karen Bitton
    Department of Ophthalmology, Rothschild Foundation Hospital, Paris, France.
  • Pierre Zéboulon
    Department of Ophthalmology, Rothschild Foundation, Paris, France. Electronic address: pierrezeboulon@gmail.com.
  • Wassim Ghazal
    Department of Ophthalmology, Rothschild Foundation, Paris, France ; and.
  • Maria Rizk
    Department of Ophthalmology, Rothschild Foundation Hospital, Paris, France.
  • Sina Elahi
    Department of Ophthalmology, Rothschild Foundation Hospital, Paris, France.
  • 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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