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Spatial classification of glaucomatous visual field loss. 1996

D B Henson, and S E Spenceley, and D R Bull
Department of Ophthalmology, University of Manchester.

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.

UI MeSH Term Description Entries
D005901 Glaucoma An ocular disease, occurring in many forms, having as its primary characteristics an unstable or a sustained increase in the intraocular pressure which the eye cannot withstand without damage to its structure or impairment of its function. The consequences of the increased pressure may be manifested in a variety of symptoms, depending upon type and severity, such as excavation of the optic disk, hardness of the eyeball, corneal anesthesia, reduced visual acuity, seeing of colored halos around lights, disturbed dark adaptation, visual field defects, and headaches. (Dictionary of Visual Science, 4th ed) Glaucomas
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D014786 Vision Disorders Visual impairments limiting one or more of the basic functions of the eye: visual acuity, dark adaptation, color vision, or peripheral vision. These may result from EYE DISEASES; OPTIC NERVE DISEASES; VISUAL PATHWAY diseases; OCCIPITAL LOBE diseases; OCULAR MOTILITY DISORDERS; and other conditions (From Newell, Ophthalmology: Principles and Concepts, 7th ed, p132). Hemeralopia,Macropsia,Micropsia,Day Blindness,Metamorphopsia,Vision Disability,Visual Disorders,Visual Impairment,Blindness, Day,Disabilities, Vision,Disability, Vision,Disorder, Visual,Disorders, Visual,Hemeralopias,Impairment, Visual,Impairments, Visual,Macropsias,Metamorphopsias,Micropsias,Vision Disabilities,Vision Disorder,Visual Disorder,Visual Impairments
D014794 Visual Fields The total area or space visible in a person's peripheral vision with the eye looking straightforward. Field, Visual,Fields, Visual,Visual Field
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D058609 Visual Field Tests Method of measuring and mapping the scope of vision, from central to peripheral of each eye. Automated Perimetry Exam,Campimetry,Perimetry,Tangent Screen Exam,Visual Field Exam,Automated Perimetry Exams,Campimetries,Exam, Automated Perimetry,Exam, Tangent Screen,Exam, Visual Field,Exams, Automated Perimetry,Exams, Tangent Screen,Exams, Visual Field,Field Exam, Visual,Field Exams, Visual,Field Test, Visual,Field Tests, Visual,Perimetries,Perimetry Exam, Automated,Perimetry Exams, Automated,Screen Exam, Tangent,Screen Exams, Tangent,Tangent Screen Exams,Test, Visual Field,Tests, Visual Field,Visual Field Exams,Visual Field Test

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