Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.

Journal: Eye (London, England)
PMID: 39789187

Abstract

PURPOSE: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-proliferative diabetic retinopathy (NPDR).

Authors

  • Giovanni Neri
    Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy.
  • Sohum Sharma
    Voxeleron Inc., Austin, USA.
  • Beatrice Ghezzo
    Department of Surgical Sciences, University of Turin, Turin, Italy.
  • Cristina Novarese
    Department of Ophthalmology, University of Turin, Via Cherasco 23, 10126 Turin, Italy.
  • Chiara Olivieri
    Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy.
  • Davide Tibaldi
    Department of Surgical Sciences, University of Turin, Turin, Italy.
  • Paola Marolo
    Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy.
  • Daniel B Russakoff
    Voxeleron LLC, Pleasanton, California, United States.
  • Jonathan D Oakley
    Voxeleron LLC, Pleasanton, California, United States.
  • Michele Reibaldi
    Division of Ophthalmology, Department of Surgical Sciences, University of Turin, Via Verdi, 8, 10124 Turin, Italy.
  • Enrico Borrelli
    Department of Surgical Sciences, University of Turin, Turin, Italy.

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