Spatial classification of glaucomatous visual field loss.
OBJECTIVE
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.
Henson
D B
DB
Department of Ophthalmology, University of Manchester.
Spenceley
S E
SE
Bull
D R
DR
D005901:Glaucoma / Q000150:complications*; D006801:Humans; D016571:Neural Networks, Computer; D014786:Vision Disorders / Q000145:classification* / Q000209:etiology; D058609:Visual Field Tests / Q000379:methods*; D014794:Visual Fields