Spatial classification of glaucomatous visual field loss

• Published in: British journal of ophthalmology Vol. 80; no. 6; pp. 526 - 531
• Main Authors: Henson, D. B., Spenceley, S. E., Bull, D. R.
• Format: Journal Article
• Language: English
• Published: BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.06.1996; BMJ; BMJ Publishing Group LTD
• 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; Human; Eye disease; Visual field disease; Glaucoma (eye); Visual field; Deficiency; Classification; Spatial representation
• ISSN: 0007-1161; 1468-2079
• DOI: 10.1136/bjo.80.6.526

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.