About: Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL

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About: Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL Goto Sponge NotDistinct Permalink

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scientific article published on 04 November 2020

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rdf:type	Item
description	wetenschappelijk artikel (nl) article scientifique publié en 2020 (fr) artículu científicu espublizáu en payares de 2020 (ast) 2020 ވަނަ އަހަރުގެ ނޮވެމްބަރުމަހުގެ 4ވަނަ ދުވަހު ޝާއިރުކުރެވުނު ސައިންޓިފިކް ލިޔުމެއް (dv) scientific article published on 04 November 2020 (en) наукова стаття, опублікована 4 листопада 2020 (uk)
publication date	wds:Q101226460-5568A235-3F65-484A-BC68-DA75B9E167F0
publication date	2020-11-04 00:00:00Z (xsd:dateTime)
cites work	wds:Q101226460-034F709C-8D6C-4F06-9296-2A77C7CE4A2A wds:Q101226460-054057F9-9D8A-4E50-87E1-0F60D43C39C0 wds:Q101226460-0B785302-4E8E-456C-A117-27F7F25A0FC8 wds:Q101226460-0FB00860-7E46-4EBE-8219-A6D7ED11FB69 wds:Q101226460-17B40F8D-7D9C-4143-977F-9D8A4BA75195 wds:Q101226460-1BCADA25-D18D-4570-A8DE-9FC4A388F99B wds:Q101226460-23419223-A648-48B6-9B24-51698E6BE35D wds:Q101226460-283E0F7D-3156-4D47-8984-E561A5E2B17A wds:Q101226460-3076A756-FB04-4934-B0F1-9DDAC38AB0C9 wds:Q101226460-36D30B11-DB4E-474C-A3C7-7BE3FCD55AC3 wds:Q101226460-3EF08268-9F71-42B4-A277-6F9C8DB6F523 wds:Q101226460-4D979999-3A69-4E0B-990D-55AC736CB5B1 wds:Q101226460-5AE74E9C-6A6F-436A-BD55-A417DA65D3E2 wds:Q101226460-64EDB295-B81D-497F-9B8D-86927BE4027B wds:Q101226460-73ADF575-5D8F-4FF6-BBF9-B3CA54E47645 wds:Q101226460-7632FE8A-BB2D-4F23-8D33-09AAE615791D wds:Q101226460-8D80E6F0-9622-4718-ABDA-35023C5EA7B8 wds:Q101226460-9C7CCE79-7EFE-4B93-BE34-1A55EBA80BC7 wds:Q101226460-A42BCACD-933C-4534-89D0-6C4BBFA82040 wds:Q101226460-A8B2E65C-CEA2-4CEB-BFF2-AE90E4561BDE wds:Q101226460-AB771FBA-FC05-4003-8FA2-114CA034B400 wds:Q101226460-ACA3E9CA-DDC3-42F9-B1F4-2AD17F0CD07C wds:Q101226460-B2FFCCF2-EF30-4B6C-B674-FC54E2212E07 wds:Q101226460-B726B45B-87EE-4848-ACBE-3D21072B9C4E wds:Q101226460-B72EEC62-C9F8-4A75-8229-ADC0936E0FC1 wds:Q101226460-BC43CF82-9A09-4123-938F-312901EA07A9 wds:Q101226460-C616438C-9428-47CB-B821-51E113C89C55 wds:Q101226460-CA9276FC-3751-4E38-A274-5DC207999454 wds:Q101226460-D58C4AF7-C549-4334-BD79-2FE43D88E73C wds:Q101226460-DA29ED6E-7520-4E87-8FD8-77E27D036E8F wds:Q101226460-E55230A2-766A-4379-B599-5874F02B65E8 wds:Q101226460-F446BCB8-31D6-43ED-8D23-75887AFF8C2E wds:Q101226460-F4E1F07E-9E4F-42BD-BE18-F45929E285E6 wds:Q101226460-FF06D136-0AD6-4DE3-807F-61CAF39FB5C9 wds:Q101226460-FF4D7006-8AE4-4092-B1AD-62CCC80B34CC
cites work	The Ocular Hypertension Treatment Study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma. The Advanced Glaucoma Intervention Study (AGIS): 7. The relationship between control of intraocular pressure and visual field deterioration.The AGIS Investigators A method to estimate the amount of neuroretinal rim tissue in glaucoma: comparison with current methods for measuring rim area Influence of clinically invisible, but optical coherence tomography detected, optic disc margin anatomy on neuroretinal rim evaluation. Longitudinal change detected by spectral domain optical coherence tomography in the optic nerve head and peripapillary retina in experimental glaucoma Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography-derived neuroretinal rim parameter. From clinical examination of the optic disc to clinical assessment of the optic nerve head: a paradigm change Minimum distance mapping using three-dimensional optical coherence tomography for glaucoma diagnosis. Morphologic changes in the lamina cribrosa correlated with neural loss in open-angle glaucoma A framework for comparing structural and functional measures of glaucomatous damage. 'Structure-function relationship' in glaucoma: past thinking and current concepts. Primary open-angle glaucoma Optic nerve damage in human glaucoma. II. The site of injury and susceptibility to damage Population-based glaucoma prevalence studies in Asians Deep learning in neural networks: an overview The association between glaucomatous visual fields and optic nerve head features in the Ocular Hypertension Treatment Study Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis Relationship between severity of visual field loss at presentation and rate of visual field progression in glaucoma Pattern of glaucomatous neuroretinal rim loss Diagnostic ability of retinal ganglion cell complex, retinal nerve fiber layer, and optic nerve head measurements by Fourier-domain optical coherence tomography Spectral-domain optical coherence tomography for early glaucoma assessment: analysis of macular ganglion cell complex versus peripapillary retinal nerve fiber layer Bruch's Membrane Opening Minimum Rim Width and Retinal Nerve Fiber Layer Thickness in a Normal White Population: A Multicenter Study. Structure-function relationships with spectral-domain optical coherence tomography retinal nerve fiber layer and optic nerve head measurements Bruch's Membrane Opening-Minimum Rim Width Assessment With Spectral-Domain Optical Coherence Tomography Performs Better Than Confocal Scanning Laser Ophthalmoscopy in Discriminating Early Glaucoma Patients From Control Subjects Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma Initial central scotomas vs peripheral scotomas in normal-tension glaucoma: clinical characteristics and progression rates Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review. Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects. Using Deep Learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images Deep-learning Classifier With an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters. Clinical Evaluation of Swedish Interactive Thresholding Algorithm-Faster Compared With Swedish Interactive Thresholding Algorithm-Standard in Normal Subjects, Glaucoma Suspects, and Patients With Glaucoma Characteristics of Patients Showing Discrepancy Between Bruch's Membrane Opening-Minimum Rim Width and Peripapillary Retinal Nerve Fiber Layer Thickness Diagnostic criteria for detection of retinal nerve fibre layer thickness and neuroretinal rim width abnormalities in glaucoma The Relationship Between Bruch's Membrane Opening-Minimum Rim Width and Retinal Nerve Fiber Layer Thickness and a New Index Using a Neural Network
author name string	wds:Q101226460-824F0161-38D5-4D9E-A93C-F760B596C0E9 wds:Q101226460-94CC82A3-B51D-40F3-AAE9-28BA132E19E0
author name string	Sat Byul Seo Hyun-Kyung Cho
rdfs:label	Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (dv) Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (en) Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (nl)
skos:prefLabel	Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (dv) Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (en) Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (nl)
name	Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (dv) Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (en) Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (nl)
title	wds:Q101226460-1CA9E725-0260-4CF1-8AE8-F613BC79D171
title	Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL (en)
page(s)	wds:Q101226460-4E9F178D-4213-4738-84F5-26CC36B17135
page(s)	19042
instance of	wds:Q101226460-36194161-8093-4221-8272-46EF8857B0A5
instance of	scholarly article
main subject	wds:Q101226460-76A96B93-609A-4CC4-9A7D-01838FDD15C4
main subject	deep learning

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