Spatial classification of glaucomatous visual field loss

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 org...

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Bibliographic Details
Published in:British journal of ophthalmology 1996-06, Vol.80 (6), p.526-531
Main Authors: Henson, D. B., Spenceley, S. E., Bull, D. R.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Glaucoma - complications
Glaucoma and intraocular pressure
Humans
Medical sciences
Neural Networks (Computer)
Ophthalmology
Vision Disorders - classification
Vision Disorders - etiology
Visual Field Tests - methods
Visual Fields
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Summary: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.
ISSN:0007-1161
1468-2079
DOI:10.1136/bjo.80.6.526