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6.30 Other (109)
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Display all abstracts in classification 6.30 Other
Search within classification 6.30 Other85127 Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma
Wang M
Ophthalmology 2020; 127: 731-738
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
84659 Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder
Berchuck SI
Scientific reports 2019; 9: 18113
85092 Altered Intrinsic Functional Connectivity of the Primary Visual Cortex in Patients with Neovascular Glaucoma: A Resting-State Functional Magnetic Resonance Imaging Study
Wu YY
Neuropsychiatric disease and treatment 2020; 16: 25-33
84806 Brain morphological alterations of cerebral cortex and subcortical nuclei in high-tension glaucoma brain and its associations with intraocular pressure
Wang Y
Neuroradiology 2020; 62: 495-502
85037 Structurally coloured contact lens sensor for point-of-care ophthalmic health monitoring
Wang Y
Journal of materials chemistry. B 2020; 0:
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
84930 New Technologies for Outcome Measures in Glaucoma: Review by the European Vision Institute Special Interest Focus Group
Traber GL
Ophthalmic Research 2020; 63: 88-96
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
84773 Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients
Lee J
Japanese Journal of Ophthalmology 2020; 64: 68-76
85016 Ophthalmic diagnosis using deep learning with fundus images - A critical review
Sengupta S
Artificial Intelligence in Medicine 2020; 102: 101758
84659 Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder
Berchuck SI
Scientific reports 2019; 9: 18113
84762 Long segment 3D double inversion recovery (DIR) hypersignal on MRI in glaucomatous optic neuropathy
Sartoretti T
BMC Ophthalmology 2019; 19: 258
84271 Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images
Xu BY
American Journal of Ophthalmology 2019; 208: 273-280
85103 Biomechanical properties of optic nerve and retrobulbar structures with 2D and 3D shear wave elastography in patients affected by glaucoma
Guazzaroni M
Clinical imaging 2020; 61: 106-114
84806 Brain morphological alterations of cerebral cortex and subcortical nuclei in high-tension glaucoma brain and its associations with intraocular pressure
Wang X
Neuroradiology 2020; 62: 495-502
85092 Altered Intrinsic Functional Connectivity of the Primary Visual Cortex in Patients with Neovascular Glaucoma: A Resting-State Functional Magnetic Resonance Imaging Study
Wang SF
Neuropsychiatric disease and treatment 2020; 16: 25-33
85037 Structurally coloured contact lens sensor for point-of-care ophthalmic health monitoring
Zhao Q
Journal of materials chemistry. B 2020; 0:
84271 Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images
Chiang M
American Journal of Ophthalmology 2019; 208: 273-280
85103 Biomechanical properties of optic nerve and retrobulbar structures with 2D and 3D shear wave elastography in patients affected by glaucoma
Ferrari D
Clinical imaging 2020; 61: 106-114
85127 Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma
Shen LQ
Ophthalmology 2020; 127: 731-738
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
84930 New Technologies for Outcome Measures in Glaucoma: Review by the European Vision Institute Special Interest Focus Group
Della Volpe-Waizel M
Ophthalmic Research 2020; 63: 88-96
84773 Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients
Kim YK
Japanese Journal of Ophthalmology 2020; 64: 68-76
