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15 Miscellaneous (212)

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80966 Glaucoma Patient Preferences for Video Education on Eye Drop Technique
Davis SA
Optometry and Vision Science 2019; 96: 325-330
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
81178 Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology
Balyen L
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2019; 8: 264-272
80660 Glaucoma Patient Knowledge, Perceptions, and Predispositions for Telemedicine
Rhodes LA
Journal of Glaucoma 2019; 28: 481-486
80904 Review of studies on the application of biomechanical factors in the evaluation of glaucoma
Song H
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 2019; 36: 315-319
81291 Association between radiation, glaucoma subtype, and retinal vessel diameter in atomic bomb survivors
Kiuchi Y
Scientific reports 2019; 9: 8642
80508 Evaluation of an Algorithm for Identifying Ocular Conditions in Electronic Health Record Data
Stein JD
JAMA ophthalmology 2019; 137: 491-497
81443 Johann Sebastian Bach's disease and his bizarre english physician
Miranda C M
Revista medica de Chile 2019; 147: 356-360
80543 Is there scope for expanding the optometrist's scope of practice in Singapore? - A survey of optometrists, opticians in Singapore
George PP
Contact Lens and Anterior Eye 2019; 42: 258-264
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
80986 Dietary Antioxidants, Macular Pigment, and Glaucomatous Neurodegeneration: A Review of the Evidence
Lawler T
Nutrients 2019; 11:
80914 Robust optic disc and cup segmentation with deep learning for glaucoma detection
Yu S
Computerized Medical Imaging and Graphics 2019; 74: 61-71
80856 Mobile Telephone Use and Reaction Time in Drivers With Glaucoma
Ogata NG
JAMA network open 2019; 2: e192169
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
81425 Places where Famous Ophthalmologist Albrecht Von Graefe Lived and Worked in Berlin
Ivanišević M
Acta medico-historica adriatica : AMHA 2019; 17: 133-142
80759 Chronotyping glaucoma patients with the Munich ChronoType Questionnaire: A case-control study
Bierings RAJM
PLoS ONE 2019; 14: e0214046
81252 Eye diseases in women
Kazama S
Fukushima Journal of Medical Science 2019; 65: 30-36
80688 CNNs for automatic glaucoma assessment using fundus images: an extensive validation
Diaz-Pinto A
Biomedical engineering online 2019; 18: 29
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
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
80967 A Novel Approach to Helping People with Glaucoma Use Their Drops Routinely
McDonald JE
Optometry and Vision Science 2019; 96: 331-334
80881 Artificial Intelligence and Optical Coherence Tomography Imaging
Kapoor R
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2019; 8: 187-194
81353 Cholinergic nervous system and glaucoma: From basic science to clinical applications
Faiq MA
Progress in Retinal and Eye Research 2019; 72: 100767
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

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