Co-Author
This page shows the publications co-authored by Tobias Elze and Mengyu Wang.
Connection Strength
10.635
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An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma. Transl Vis Sci Technol. 2020 08; 9(9):41.
Score: 0.891
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Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence. JAMA Ophthalmol. 2020 02 01; 138(2):190-198.
Score: 0.857
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Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma. Ophthalmology. 2020 06; 127(6):731-738.
Score: 0.849
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An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis. Invest Ophthalmol Vis Sci. 2019 01 02; 60(1):365-375.
Score: 0.795
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Reply. Ophthalmology. 2018 09; 125(9):e66-e67.
Score: 0.775
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Quantifying positional variation of retinal blood vessels in glaucoma. PLoS One. 2018; 13(3):e0193555.
Score: 0.752
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The Interrelationship between Refractive Error, Blood Vessel Anatomy, and Glaucomatous Visual Field Loss. Transl Vis Sci Technol. 2018 Jan; 7(1):4.
Score: 0.744
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Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 1. Impacts of refractive error and interartery angle. J Biomed Opt. 2017 Dec; 22(12):1-11.
Score: 0.737
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Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma. Ophthalmology. 2018 03; 125(3):352-360.
Score: 0.733
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Impact of Natural Blind Spot Location on Perimetry. Sci Rep. 2017 07 21; 7(1):6143.
Score: 0.719
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Relationship Between Central Retinal Vessel Trunk Location and Visual Field Loss in Glaucoma. Am J Ophthalmol. 2017 Apr; 176:53-60.
Score: 0.693
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The Effect of Ametropia on Glaucomatous Visual Field Loss. J Clin Med. 2021 Jun 25; 10(13).
Score: 0.236
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Predicting Global Test-Retest Variability of Visual Fields in Glaucoma. Ophthalmol Glaucoma. 2021 Jul-Aug; 4(4):390-399.
Score: 0.227
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Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility. Transl Vis Sci Technol. 2020 11; 9(12):22.
Score: 0.226
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Norms of Interocular Circumpapillary Retinal Nerve Fiber Layer Thickness Differences at 768 Retinal Locations. Transl Vis Sci Technol. 2020 08; 9(9):23.
Score: 0.222
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Sex-Specific Differences in Circumpapillary Retinal Nerve Fiber Layer Thickness. Ophthalmology. 2020 03; 127(3):357-368.
Score: 0.209
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Predicting Refractive Outcome of Small Incision Lenticule Extraction for Myopia Using Corneal Properties. Transl Vis Sci Technol. 2018 Sep; 7(5):11.
Score: 0.195
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Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 2. Impacts of optic nerve head parameters. J Biomed Opt. 2017 Dec; 22(12):1-9.
Score: 0.184
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Age, ocular magnification, and circumpapillary retinal nerve fiber layer thickness. J Biomed Opt. 2017 12; 22(12):1-19.
Score: 0.184
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Associations between Optic Nerve Head-Related Anatomical Parameters and Refractive Error over the Full Range of Glaucoma Severity. Transl Vis Sci Technol. 2017 Jul; 6(4):9.
Score: 0.180
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Renal function and lipid metabolism are major predictors of circumpapillary retinal nerve fiber layer thickness-the LIFE-Adult Study. BMC Med. 2021 09 07; 19(1):202.
Score: 0.060
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Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence. Transl Vis Sci Technol. 2021 08 02; 10(9):16.
Score: 0.059
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Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard. Ophthalmology. 2020 09; 127(9):1170-1178.
Score: 0.054
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The impact of artificial intelligence in the diagnosis and management of glaucoma. Eye (Lond). 2020 01; 34(1):1-11.
Score: 0.052
Connection Strength
The connection strength for co-authors is the sum of the scores for each of their shared publications.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.