A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

Journal: American journal of ophthalmology

Published Date: May 1, 2019

Abstract

PURPOSE: To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT).

Authors

Atalie C Thompson

Vision, Imaging and Performance (VIP) Laboratory, Duke Eye Center and Department of Ophthalmology, Duke University, Durham, North Carolina.
Alessandro A Jammal

Vision, Imaging and Performance (VIP) Laboratory, Duke Eye Center and Department of Ophthalmology, Duke University, Durham, North Carolina.
Felipe A Medeiros

Duke Eye Center, Department of Ophthalmology, Duke University, Durham, North Carolina, United States.

Keywords

Aged Aged, 80 and over Algorithms Area Under Curve Bruch Membrane Cross-Sectional Studies Deep Learning Female Glaucoma, Open-Angle Humans Intraocular Pressure Male Middle Aged Nerve Fibers Optic Disk Optic Nerve Diseases Photography Retinal Ganglion Cells Retrospective Studies Tomography, Optical Coherence Vision Disorders Visual Field Tests Visual Fields

External Resources

View on PubMed Access via DOI PubMed (30689990)

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

Abstract

Authors

Keywords

External Resources

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