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WGW-2020

6.8.2 Posterior segment (73)

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79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Ahn JM
PLoS ONE 2018; 13: e0207982
79898 Screening Glaucoma With Red-free Fundus Photography Using Deep Learning Classifier and Polar Transformation
Lee J
Journal of Glaucoma 2019; 28: 258-264
79662 Nonmydriatic Fundus Photography in Patients with Acute Vision Loss
Vasseneix C
Telemedicine Journal and E-Health: the Official Journal of the American Telemedicine Association 2018; 0:
79930 Agreement study between color and IR retinal images based on retinal vasculature morphological parameters
Ajaz A
BMC Ophthalmology 2019; 19: 27
79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Christopher M
Scientific reports 2018; 8: 16685
80006 Non-physician grader reliability in measuring morphological features of the optic nerve head in stereo digital images
Addis V
Eye 2019; 33: 838-844
79559 Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques
Maheshwari S
Computers in Biology and Medicine 2019; 105: 72-80
80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
MacCormick IJC
PLoS ONE 2019; 14: e0209409
79863 A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs
Thompson AC
American Journal of Ophthalmology 2019; 201: 9-18
79704 Artificial intelligence in glaucoma
Zheng C
Current Opinions in Ophthalmology 2019; 30: 97-103
80071 Factors Associated with Progression of Japanese Open-Angle Glaucoma with Lower Normal Intraocular Pressure
Sakata R
Ophthalmology 2018; 0:
79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Kim S
PLoS ONE 2018; 13: e0207982
79930 Agreement study between color and IR retinal images based on retinal vasculature morphological parameters
Aliahmad B
BMC Ophthalmology 2019; 19: 27
79704 Artificial intelligence in glaucoma
Johnson TV
Current Opinions in Ophthalmology 2019; 30: 97-103
80071 Factors Associated with Progression of Japanese Open-Angle Glaucoma with Lower Normal Intraocular Pressure
Yoshitomi T
Ophthalmology 2018; 0:
80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
Williams BM
PLoS ONE 2019; 14: e0209409
79863 A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs
Jammal AA
American Journal of Ophthalmology 2019; 201: 9-18
79898 Screening Glaucoma With Red-free Fundus Photography Using Deep Learning Classifier and Polar Transformation
Kim Y
Journal of Glaucoma 2019; 28: 258-264
79662 Nonmydriatic Fundus Photography in Patients with Acute Vision Loss
Bruce BB
Telemedicine Journal and E-Health: the Official Journal of the American Telemedicine Association 2018; 0:
79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Belghith A
Scientific reports 2018; 8: 16685
80006 Non-physician grader reliability in measuring morphological features of the optic nerve head in stereo digital images
Oyeniran E
Eye 2019; 33: 838-844
79559 Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques
Kanhangad V
Computers in Biology and Medicine 2019; 105: 72-80
79863 A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs
Medeiros FA
American Journal of Ophthalmology 2019; 201: 9-18
79930 Agreement study between color and IR retinal images based on retinal vasculature morphological parameters
Kumar H
BMC Ophthalmology 2019; 19: 27
80006 Non-physician grader reliability in measuring morphological features of the optic nerve head in stereo digital images
Daniel E
Eye 2019; 33: 838-844

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WGW-2020