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15 Miscellaneous (214)
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Display all abstracts in classification 15 Miscellaneous
Search within classification 15 Miscellaneous80934 Ocular hypotony: A comprehensive review
Wang Q
Survey of Ophthalmology 2019; 64: 619-638
81090 Central retinal venous pressure is higher than intraocular pressure during amateur trumpet playing
Matthé E
Graefe's Archive for Clinical and Experimental Ophthalmology 2019; 257: 1467-1472
80630 Machine learning models based on the dimensionality reduction of standard automated perimetry data for glaucoma diagnosis
Lee SD
Artificial Intelligence in Medicine 2019; 94: 110-116
81178 Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology
Balyen L
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2019; 8: 264-272
80966 Glaucoma Patient Preferences for Video Education on Eye Drop Technique
Davis SA
Optometry and Vision Science 2019; 96: 325-330
81430 A Randomized Trial to Increase the Assessment Accuracy of Glaucoma and Optic Disc Characteristics by Orthoptists
Scheetz J
The Journal of continuing education in the health professions 2019; 39: 161-167
81252 Eye diseases in women
Kazama S
Fukushima Journal of Medical Science 2019; 65: 30-36
80881 Artificial Intelligence and Optical Coherence Tomography Imaging
Kapoor R
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2019; 8: 187-194
80688 CNNs for automatic glaucoma assessment using fundus images: an extensive validation
Diaz-Pinto A
Biomedical engineering online 2019; 18: 29
81029 Illness perceptions in people newly diagnosed with glaucoma and ocular hypertension
McDonald L
British Journal of Ophthalmology 2020; 104: 110-114
81465 Dynamic Monitoring and Control of Irreversible Chronic Diseases with Application to Glaucoma
Kazemian P
Production and operations management 2019; 28: 1082-1107
80583 Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment
Diaz-Pinto A
IEEE Transactions on Medical Imaging 2019; 38: 2211-2218
80518 Evaluation of deep convolutional neural networks for glaucoma detection
Phan S
Japanese Journal of Ophthalmology 2019; 63: 276-283
80856 Mobile Telephone Use and Reaction Time in Drivers With Glaucoma
Ogata NG
JAMA network open 2019; 2: e192169
80746 Short-Term Safety Evaluation of a Multi-Pressure Dial: A Prospective, Open-label, Non-randomized Study
Thompson VM
Ophthalmology and therapy 2019; 8: 279-287
80913 JointRCNN: A Region-based Convolutional Neural Network for Optic Disc and Cup Segmentation
Jiang Y
IEEE Transactions on Bio-Medical Engineering 2020; 67: 335-343
81425 Places where Famous Ophthalmologist Albrecht Von Graefe Lived and Worked in Berlin
Ivanišević M
Acta medico-historica adriatica : AMHA 2019; 17: 133-142
81315 Rapid network meta-analysis using data from Food and Drug Administration approval packages is feasible but with limitations
Wang L
Journal of Clinical Epidemiology 2019; 114: 84-94
81186 Face memory deficits in subjects with eye diseases: a comparative analysis between glaucoma and age-related macular degeneration patients from a developing country
Mazzoli LS
Graefe's Archive for Clinical and Experimental Ophthalmology 2019; 257: 1941-1946
80660 Glaucoma Patient Knowledge, Perceptions, and Predispositions for Telemedicine
Rhodes LA
Journal of Glaucoma 2019; 28: 481-486
80445 Ophthalmology specialist trainee survey in the United Kingdom
Dean WH
Eye 2019; 33: 917-924
80967 A Novel Approach to Helping People with Glaucoma Use Their Drops Routinely
McDonald JE
Optometry and Vision Science 2019; 96: 331-334
80914 Robust optic disc and cup segmentation with deep learning for glaucoma detection
Yu S
Computerized Medical Imaging and Graphics 2019; 74: 61-71
80562 Feasibility of simple machine learning approaches to support detection of non-glaucomatous visual fields in future automated glaucoma clinics
Thomas PBM
Eye 2019; 33: 1133-1139
80734 Assessing driving risk in patients with glaucoma
Correa PC
Arquivos Brasileiros de Oftalmologia 2019; 82: 245-252
