Literature DB >> 5154258

Visual field defects in early open angle glaucoma.

M F Armaly.   

Abstract

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Year:  1971        PMID： 5154258      PMCID： PMC1310411     

Source DB:  PubMed          Journal:  Trans Am Ophthalmol Soc        ISSN： 0065-9533


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  2 in total

1.  The visual field defect and ocular pressure level in open angle glaucoma.

Authors:  M F Armaly
Journal:  Invest Ophthalmol       Date:  1969-02

2.  Ocular pressure and visual fields. A ten-year follow-up study.

Authors:  M F Armaly
Journal:  Arch Ophthalmol       Date:  1969-01
  2 in total
  10 in total

1.  Temporal visual field in glaucoma: a re-evaluation in the automated perimetry era.

Authors:  G E Pennebaker; W C Stewart
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  1992       Impact factor: 3.117

2.  Imaging Glaucomatous Damage Across the Temporal Raphe.

Authors:  Gang Huang; Ting Luo; Thomas J Gast; Stephen A Burns; Victor E Malinovsky; William H Swanson
Journal:  Invest Ophthalmol Vis Sci       Date:  2015-06       Impact factor: 4.799

3.  In vivo adaptive optics imaging of the temporal raphe and its relationship to the optic disc and fovea in the human retina.

Authors:  Gang Huang; Thomas J Gast; Stephen A Burns
Journal:  Invest Ophthalmol Vis Sci       Date:  2014-08-21       Impact factor: 4.799

4.  Pattern noise (PANO): a new automated functional glaucoma test.

Authors:  Sylvain El-Khoury; Thomas Hannen; Diana Carmen Dragnea; Faustin Ngounou; Paul-Rolf Preußner
Journal:  Int Ophthalmol       Date:  2017-08-16       Impact factor: 2.031

5.  Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements.

Authors:  Siamak Yousefi; Michael H Goldbaum; Madhusudhanan Balasubramanian; Felipe A Medeiros; Linda M Zangwill; Jeffrey M Liebmann; Christopher A Girkin; Robert N Weinreb; Christopher Bowd
Journal:  IEEE Trans Biomed Eng       Date:  2014-04-01       Impact factor: 4.538

6.  Recognizing patterns of visual field loss using unsupervised machine learning.

Authors:  Siamak Yousefi; Michael H Goldbaum; Linda M Zangwill; Felipe A Medeiros; Christopher Bowd
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2014-03-21

7.  Evaluation of the Friedmann Visual Field Analyser Mark II. Part 1. Results from a normal population.

Authors:  D B Henson; S M Dix; A C Oborne
Journal:  Br J Ophthalmol       Date:  1984-07       Impact factor: 4.638

8.  Use of high spatial resolution perimetry to identify scotomata not apparent with conventional perimetry in the nasal field of glaucomatous subjects.

Authors:  M C Westcott; D F Garway-Heath; F W Fitzke; D Kamal; R A Hitchings
Journal:  Br J Ophthalmol       Date:  2002-07       Impact factor: 4.638

9.  Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

Authors:  Christopher Bowd; Robert N Weinreb; Madhusudhanan Balasubramanian; Intae Lee; Giljin Jang; Siamak Yousefi; Linda M Zangwill; Felipe A Medeiros; Christopher A Girkin; Jeffrey M Liebmann; Michael H Goldbaum
Journal:  PLoS One       Date:  2014-01-30       Impact factor: 3.240

10.  Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects.

Authors:  Pamela A Sample; Kwokleung Chan; Catherine Boden; Te-Won Lee; Eytan Z Blumenthal; Robert N Weinreb; Antje Bernd; John Pascual; Jiucang Hao; Terrence Sejnowski; Michael H Goldbaum
Journal:  Invest Ophthalmol Vis Sci       Date:  2004-08       Impact factor: 4.799
  10 in total

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