Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging.

Journal: Acta ophthalmologica
PMID: 38682863

Abstract

PURPOSE: To elaborate a deep learning (DL) model for automatic prediction of late recurrence (LR) of rhegmatogenous retinal detachment (RRD) using pseudocolor and fundus autofluorescence (AF) ultra-wide field (UWF) images obtained preoperatively and postoperatively.

Authors

  • Fiammetta Catania
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.
  • Thibaut Chapron
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.
  • Emanuele Crincoli
    Ophthalmology Department, "Fondazione Policlinico Universitario A. Gemelli, IRCCS", Rome, Italy.
  • Alexandra Miere
    Department of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, Créteil, France; Laboratory of Images, Signals and Intelligent Systems (LISSI), (EA N° 3956), University Paris-Est, Créteil, France. Electronic address: alexandra.miere@chicreteil.fr.
  • Youssef Abdelmassih
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.
  • William Beaumont
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.
  • Ismael Chehaibou
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.
  • Florence Metge
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.
  • Sebastien Bruneau
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.
  • Sophie Bonnin
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.
  • Eric H Souied
    Department of Ophthalmology, Hôpital Intercommunal de Créteil, Université, Creteil, France.
  • Georges Caputo
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.

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