Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.

Journal: Eye (London, England)

PMID: 37670143

Abstract

PURPOSE: To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).

Authors

Enrico Borrelli

Department of Surgical Sciences, University of Turin, Turin, Italy.
Jonathan D Oakley

Voxeleron LLC, Pleasanton, California, United States.
Giorgio Iaccarino

Vita-Salute San Raffaele University Milan, Milan, Italy.
Daniel B Russakoff

Voxeleron LLC, Pleasanton, California, United States.
Marco Battista

Vita-Salute San Raffaele University Milan, Milan, Italy.
Domenico Grosso

Vita-Salute San Raffaele University Milan, Milan, Italy.
Federico Borghesan

Vita-Salute San Raffaele University Milan, Milan, Italy.
Costanza Barresi

Vita-Salute San Raffaele University Milan, Milan, Italy.
Riccardo Sacconi

Department of Ophthalmology, IRCCS Ospedale San Raffaele, University Vita-Salute, Milan, Italy.
Francesco Bandello

Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, via Olgettina 60, 20132, Milan, Italy.
Giuseppe Querques

Department of Ophthalmology, IRCCS Ospedale San Raffaele, University Vita-Salute, Milan, Italy.

Keywords

Angiogenesis Inhibitors Deep Learning Humans Intravitreal Injections Macular Degeneration Ranibizumab Retinal Detachment Retrospective Studies Subretinal Fluid Tomography, Optical Coherence Wet Macular Degeneration

External Resources

View on PubMed Access via DOI PubMed (37670143)

Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.

Abstract

Authors

Keywords

External Resources

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