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6.8.2 Posterior segment (71)
Showing records 1 to 25
Display all abstracts in classification 6.8.2 Posterior segment
Search within classification 6.8.2 Posterior segment80006 Non-physician grader reliability in measuring morphological features of the optic nerve head in stereo digital images
Addis V
Eye 2019; 33: 838-844
79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Christopher M
Scientific reports 2018; 8: 16685
79559 Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques
Maheshwari S
Computers in Biology and Medicine 2019; 105: 72-80
79898 Screening Glaucoma With Red-free Fundus Photography Using Deep Learning Classifier and Polar Transformation
Lee J
Journal of Glaucoma 2019; 28: 258-264
79930 Agreement study between color and IR retinal images based on retinal vasculature morphological parameters
Ajaz A
BMC Ophthalmology 2019; 19: 27
80071 Factors Associated with Progression of Japanese Open-Angle Glaucoma with Lower Normal Intraocular Pressure
Sakata R
Ophthalmology 2019; 126: 1107-1116
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 2019; 25: 911-916
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
79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Ahn JM
PLoS ONE 2018; 13: e0207982
79704 Artificial intelligence in glaucoma
Zheng C
Current Opinions in Ophthalmology 2019; 30: 97-103
79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Kim S
PLoS ONE 2018; 13: e0207982
80071 Factors Associated with Progression of Japanese Open-Angle Glaucoma with Lower Normal Intraocular Pressure
Yoshitomi T
Ophthalmology 2019; 126: 1107-1116
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
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
79704 Artificial intelligence in glaucoma
Johnson TV
Current Opinions in Ophthalmology 2019; 30: 97-103
79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Belghith A
Scientific reports 2018; 8: 16685
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
79930 Agreement study between color and IR retinal images based on retinal vasculature morphological parameters
Aliahmad B
BMC Ophthalmology 2019; 19: 27
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 2019; 25: 911-916
79863 A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs
Medeiros FA
American Journal of Ophthalmology 2019; 201: 9-18
80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
Zheng Y
PLoS ONE 2019; 14: e0209409
79662 Nonmydriatic Fundus Photography in Patients with Acute Vision Loss
Bidot S
Telemedicine Journal and E-Health: the Official Journal of the American Telemedicine Association 2019; 25: 911-916
