Literature DB >> 8759263

Spatial classification of glaucomatous visual field loss.

D B Henson1, S E Spenceley, D R Bull.   

Abstract

AIMS: To develop and describe an objective classification system for the spatial patterns of visual field loss found in glaucoma.
METHODS: The 560 Humphrey visual field analyser (program 24-2) records were used to train an artificial neural network (ANN). The type of network used, a Kohonen self organising feature map (SOM), was configured to organise the visual field defects into 25 classes of superior visual field loss and 25 classes of inferior visual field loss. Each group of 25 classes was arranged in a 5 by 5 map.
RESULTS: The SOM successfully classified the defects on the basis of the patterns of loss. The maps show a continuum of change as one moves across them with early loss at one corner and advanced loss at the opposite corner.
CONCLUSIONS: ANNs can classify visual field data on the basis of the pattern of loss. Once trained the ANN can be used to classify longitudinal visual field data which may prove valuable in monitoring visual field loss.

Entities:  

Mesh:

Year:  1996        PMID: 8759263      PMCID: PMC505525          DOI: 10.1136/bjo.80.6.526

Source DB:  PubMed          Journal:  Br J Ophthalmol        ISSN: 0007-1161            Impact factor:   4.638


  6 in total

1.  The early field defects in glaucoma.

Authors:  S M Drance
Journal:  Invest Ophthalmol       Date:  1969-02

2.  The rate of visual field loss in untreated primary open angle glaucoma.

Authors:  J L Jay; J R Murdoch
Journal:  Br J Ophthalmol       Date:  1993-03       Impact factor: 4.638

3.  Visual field interpretation with a personal computer based neural network.

Authors:  E Mutlukan; D Keating
Journal:  Eye (Lond)       Date:  1994       Impact factor: 3.775

4.  The onset and evolution of glaucomatous visual field defects.

Authors:  W M Hart; B Becker
Journal:  Ophthalmology       Date:  1982-03       Impact factor: 12.079

5.  Visual field analysis using artificial neural networks.

Authors:  S E Spenceley; D B Henson; D R Bull
Journal:  Ophthalmic Physiol Opt       Date:  1994-07       Impact factor: 3.117

6.  Interpretation of automated perimetry for glaucoma by neural network.

Authors:  M H Goldbaum; P A Sample; H White; B Côlt; P Raphaelian; R D Fechtner; R N Weinreb
Journal:  Invest Ophthalmol Vis Sci       Date:  1994-08       Impact factor: 4.799

  6 in total
  12 in total

1.  Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.

Authors:  Michael H Goldbaum; Pamela A Sample; Zuohua Zhang; Kwokleung Chan; Jiucang Hao; Te-Won Lee; Catherine Boden; Christopher Bowd; Rupert Bourne; Linda Zangwill; Terrence Sejnowski; David Spinak; Robert N Weinreb
Journal:  Invest Ophthalmol Vis Sci       Date:  2005-10       Impact factor: 4.799

2.  Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects.

Authors:  Michael Henry Goldbaum
Journal:  Trans Am Ophthalmol Soc       Date:  2005

Review 3.  Detection of visual field progression in glaucoma with standard achromatic perimetry: a review and practical implications.

Authors:  Kouros Nouri-Mahdavi; Nariman Nassiri; Annette Giangiacomo; Joseph Caprioli
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2011-08-26       Impact factor: 3.117

4.  Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements.

Authors:  Christopher Bowd; Intae Lee; Michael H Goldbaum; Madhusudhanan Balasubramanian; Felipe A Medeiros; Linda M Zangwill; Christopher A Girkin; Jeffrey M Liebmann; Robert N Weinreb
Journal:  Invest Ophthalmol Vis Sci       Date:  2012-04-30       Impact factor: 4.799

5.  Baseline visual field findings in the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT).

Authors:  John L Keltner; Chris A Johnson; Kimberly E Cello; Michael Wall
Journal:  Invest Ophthalmol Vis Sci       Date:  2014-04-29       Impact factor: 4.799

6.  Patterns of functional vision loss in glaucoma determined with archetypal analysis.

Authors:  Tobias Elze; Louis R Pasquale; Lucy Q Shen; Teresa C Chen; Janey L Wiggs; Peter J Bex
Journal:  J R Soc Interface       Date:  2015-02-06       Impact factor: 4.118

7.  Computerized expert system for evaluation of automated visual fields from the Ischemic Optic Neuropathy Decompression Trial: methods, baseline fields, and six-month longitudinal follow-up.

Authors:  Steven E Feldon
Journal:  Trans Am Ophthalmol Soc       Date:  2004

8.  Assessing visual field clustering schemes using machine learning classifiers in standard perimetry.

Authors:  Catherine Boden; Kwokleung Chan; Pamela A Sample; Jiucang Hao; Te-Wan Lee; Linda M Zangwill; Robert N Weinreb; Michael H Goldbaum
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-12       Impact factor: 4.799

9.  Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.

Authors:  Pamela A Sample; Catherine Boden; Zuohua Zhang; John Pascual; Te-Won Lee; Linda M Zangwill; Robert N Weinreb; Jonathan G Crowston; Esther M Hoffmann; Felipe A Medeiros; Terrence Sejnowski; Michael Goldbaum
Journal:  Invest Ophthalmol Vis Sci       Date:  2005-10       Impact factor: 4.799

10.  Development and validation of a computerized expert system for evaluation of automated visual fields from the Ischemic Optic Neuropathy Decompression Trial.

Authors:  Steven E Feldon; Lori Levin; Roberta W Scherer; Anthony Arnold; Sophia M Chung; Lenworth N Johnson; Gregory Kosmorsky; Steven A Newman; Joanne Katz; Patricia Langenberg; P David Wilson; Shalom E Kelman; Kay Dickersin
Journal:  BMC Ophthalmol       Date:  2006-11-20       Impact factor: 2.209

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