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

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). Cross-sectional study. A total of 9282 pairs of optic dis...

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Vydané v:	American journal of ophthalmology Ročník 201; s. 9 - 18
Hlavní autori:	Thompson, Atalie C., Jammal, Alessandro A., Medeiros, Felipe A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 01.05.2019 Elsevier Limited
Predmet:	Aged Aged, 80 and over Algorithms Area Under Curve Bruch Membrane - pathology Cross-Sectional Studies Deep Learning Diabetic retinopathy Female Glaucoma Glaucoma, Open-Angle - diagnosis Glaucoma, Open-Angle - physiopathology Humans Hypertension Intraocular Pressure - physiology Male Middle Aged Nerve Fibers - pathology Neural networks Ophthalmology Optic Disk - diagnostic imaging Optic nerve Optic Nerve Diseases - diagnosis Optic Nerve Diseases - physiopathology Optics Photography - methods Retinal Ganglion Cells - pathology Retrospective Studies Software Tomography, Optical Coherence Vision Disorders - diagnosis Vision Disorders - physiopathology Visual Field Tests Visual Fields - physiology
ISSN:	0002-9394, 1879-1891, 1879-1891
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