DEEP LEARNING FOR AUTOMATIC PREDICTION OF EARLY ACTIVATION OF TREATMENT-NAIVE NONEXUDATIVE MACULAR NEOVASCULARIZATIONS IN AGE-RELATED MACULAR DEGENERATION.

Journal: Retina (Philadelphia, Pa.)
PMID: 38489765

Abstract

BACKGROUND: Around 30% of nonexudative macular neovascularizations exudate within 2 years from diagnosis in patients with age-related macular degeneration. The aim of this study is to develop a deep learning classifier based on optical coherence tomography (OCT) and OCT angiography (OCTA) to identify nonexudative macular neovascularizations at risk of exudation.

Authors

  • Emanuele Crincoli
    Ophthalmology Department, "Fondazione Policlinico Universitario A. Gemelli, IRCCS", Rome, Italy.
  • Fiammetta Catania
    Ophthalmology Department, Rothschild Foundation Hospital, Paris, France.
  • Riccardo Sacconi
    Department of Ophthalmology, IRCCS Ospedale San Raffaele, University Vita-Salute, Milan, Italy.
  • Nicolò Ribarich
    Department of Ophthalmology University Vita-Salute IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
  • Silvia Ferrara
    Department of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy.
  • Mariacristina Parravano
    IRCCS - Fondazione Bietti, Rome, Italy.
  • Eliana Costanzo
    IRCCS - Fondazione Bietti, Rome, Italy.
  • Giuseppe Querques
    Department of Ophthalmology, IRCCS Ospedale San Raffaele, University Vita-Salute, Milan, Italy.

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