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6.30 Other (81)
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Search within classification 6.30 Other86851 Artificial Intelligence for Cataract Detection and Management
Goh JHL
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95
86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Yang HK
American Journal of Ophthalmology 2020; 216: 140-146
86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Barros DMS
Biomedical engineering online 2020; 19: 20
86850 Artificial Intelligence in Ophthalmology: Evolutions in Asia
Ruamviboonsuk P
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 78-84
86835 Using Deep Learning to Automate Goldmann Applanation Tonometry Readings
Spaide T
Ophthalmology 2020; 127: 1498-1506
86478 In Vivo Measurements of Prelamina and Lamina Cribrosa Biomechanical Properties in Humans
Zhang L
Investigative Ophthalmology and Visual Science 2020; 61: 27
86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Normando EM
Expert review of molecular diagnostics 2020; 20: 737-748
86430 Altered fMRI-derived functional connectivity in patients with high-tension glaucoma
Wang B
Journal of neuroradiology. Journal de neuroradiologie 2021; 48: 94-98
86746 Comparison of Macular Pigment Optical Density in Glaucoma Patients and Healthy Subjects - A Prospective Diagnostic Study
Bruns Y
Clinical Ophthalmology 2020; 14: 1011-1017
86766 Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard
Yousefi S
Ophthalmology 2020; 127: 1170-1178
86727 Using the entropy of the corneal pulse signal to distinguish healthy eyes from eyes affected by primary open-angle glaucoma
Danielewska ME
Physiological Measurement 2020; 41: 055011
86850 Artificial Intelligence in Ophthalmology: Evolutions in Asia
Cheung CY
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 78-84
86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Moura JCC
Biomedical engineering online 2020; 19: 20
86766 Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard
Elze T
Ophthalmology 2020; 127: 1170-1178
86727 Using the entropy of the corneal pulse signal to distinguish healthy eyes from eyes affected by primary open-angle glaucoma
Placek MM
Physiological Measurement 2020; 41: 055011
86430 Altered fMRI-derived functional connectivity in patients with high-tension glaucoma
Yan T
Journal of neuroradiology. Journal de neuroradiologie 2021; 48: 94-98
86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Yap TE
Expert review of molecular diagnostics 2020; 20: 737-748
86835 Using Deep Learning to Automate Goldmann Applanation Tonometry Readings
Wu Y
Ophthalmology 2020; 127: 1498-1506
86478 In Vivo Measurements of Prelamina and Lamina Cribrosa Biomechanical Properties in Humans
Beotra MR
Investigative Ophthalmology and Visual Science 2020; 61: 27
86851 Artificial Intelligence for Cataract Detection and Management
Lim ZW
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95
86746 Comparison of Macular Pigment Optical Density in Glaucoma Patients and Healthy Subjects - A Prospective Diagnostic Study
Junker B
Clinical Ophthalmology 2020; 14: 1011-1017
86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Kim YJ
American Journal of Ophthalmology 2020; 216: 140-146
86766 Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard
Pasquale LR
Ophthalmology 2020; 127: 1170-1178
86430 Altered fMRI-derived functional connectivity in patients with high-tension glaucoma
Zhou J
Journal of neuroradiology. Journal de neuroradiologie 2021; 48: 94-98
86746 Comparison of Macular Pigment Optical Density in Glaucoma Patients and Healthy Subjects - A Prospective Diagnostic Study
Boehringer D
Clinical Ophthalmology 2020; 14: 1011-1017
