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6.9.5 Other (70)

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84979 Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and Meta-analysis
Murtagh P
International Journal of Ophthalmology 2020; 13: 149-162
84734 Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior
Zhou W
Computational and mathematical methods in medicine 2019; 2019: 8973287
84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Jammal AA
American Journal of Ophthalmology 2020; 211: 123-131
84090 Glaucoma Detection from Retinal Images Using Statistical and Textural Wavelet Features
Abdel-Hamid L
Journal of digital imaging 2020; 33: 151-158
85106 Hierarchical Cluster Analysis of Peripapillary Retinal Nerve Fiber Layer Damage and Macular Ganglion Cell Loss in Open Angle Glaucoma
Lee K
Korean Journal of Ophthalmology 2020; 34: 56-66
85101 Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis
Muramatsu C
Adv Exp Med Biol 2020; 1213: 121-132
85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Li F
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867
84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Li W
Journal of Glaucoma 2020; 29: 81-85
84506 Regional Patterns in Retinal Microvascular Network Geometry in Health and Disease
Popovic N
Scientific reports 2019; 9: 16340
84814 Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification
Ha A
Scientific reports 2019; 9: 19771
85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Zapata MA
Clinical Ophthalmology 2020; 14: 419-429
85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Bambo MP
BMC Ophthalmology 2020; 20: 35
85106 Hierarchical Cluster Analysis of Peripapillary Retinal Nerve Fiber Layer Damage and Macular Ganglion Cell Loss in Open Angle Glaucoma
Bae HW
Korean Journal of Ophthalmology 2020; 34: 56-66
84506 Regional Patterns in Retinal Microvascular Network Geometry in Health and Disease
Vujosevic S
Scientific reports 2019; 9: 16340
85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Fuentemilla E
BMC Ophthalmology 2020; 20: 35
85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Royo-Fibla D
Clinical Ophthalmology 2020; 14: 419-429
84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Thompson AC
American Journal of Ophthalmology 2020; 211: 123-131
84734 Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior
Yi Y
Computational and mathematical methods in medicine 2019; 2019: 8973287
84814 Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification
Sun S
Scientific reports 2019; 9: 19771
84979 Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and Meta-analysis
Greene G
International Journal of Ophthalmology 2020; 13: 149-162
85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Yan L
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867
84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Chen Q
Journal of Glaucoma 2020; 29: 81-85
84814 Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification
Kim YK
Scientific reports 2019; 9: 19771
85106 Hierarchical Cluster Analysis of Peripapillary Retinal Nerve Fiber Layer Damage and Macular Ganglion Cell Loss in Open Angle Glaucoma
Lee SY
Korean Journal of Ophthalmology 2020; 34: 56-66
84979 Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and Meta-analysis
O'Brien C
International Journal of Ophthalmology 2020; 13: 149-162

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