Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs

To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs. Evaluation of a machine learning algorithm. An M2M DL algorithm trained with R...

Full description

Saved in:

Bibliographic Details
Published in:	American journal of ophthalmology Vol. 211; pp. 123 - 131
Main Authors:	Jammal, Alessandro A., Thompson, Atalie C., Mariottoni, Eduardo B., Berchuck, Samuel I., Urata, Carla N., Estrela, Tais, Wakil, Susan M., Costa, Vital P., Medeiros, Felipe A.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.03.2020 Elsevier Limited
Subjects:	Aged Algorithms Area Under Curve Cross-Sectional Studies Datasets Deep Learning Defects Diabetic retinopathy Female Fundus Oculi Glaucoma Glaucoma, Open-Angle - diagnosis Glaucoma, Open-Angle - diagnostic imaging Gonioscopy Humans Intraocular Pressure - physiology Male Middle Aged Nerve Fibers - pathology Neural networks Optic nerve Optic Nerve Diseases - diagnosis Optic Nerve Diseases - diagnostic imaging Optics Photography Physical Examination Retinal Ganglion Cells - pathology Retrospective Studies ROC Curve Software Tomography Tomography, Optical Coherence Vision Disorders - diagnosis Visual Field Tests - methods Visual Fields - physiology
ISSN:	0002-9394, 1879-1891, 1879-1891
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!