Biomaterial Database

Neural network differentiation of optic neuritis and anterior ischaemic optic neuropathy. 1996

L A Levin, and J F Rizzo, and S Lessell

Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, USA.

OBJECTIVE The efficacy of an artificial intelligence technique, neural network analysis, was examined in differentiating two optic neuropathies with overlapping clinical profiles-idiopathic optic neuritis (ON) and non-arteritic anterior ischaemic optic neuropathy (AION). METHODS A neural network was trained with data from 116 patients with 'gold standard' diagnoses of ON or AION. It was then tested with data from 128 patients with presumed ON or AION, and the correlation of the network's diagnosis with that of expert clinicians tabulated. RESULTS The network agreed with the clinicians on 97.8% (88 of 90) of the patients with presumed ON and 94.7% (36 of 38) of the patients with presumed AION. Youth, female sex, better initial acuity, a central scotoma, subsequent improvement in acuity, or progressive disease biased the network towards a diagnosis of ON, while advanced age, male sex, presence of hypertension, poor initial acuity, an altitudinal field defect, disc oedema, or less improvement in acuity biased the network towards a diagnosis of AION. CONCLUSIONS Neural network analysis is a useful technique for classification of optic neuropathies, particularly where there is overlap of clinical findings.

UI	MeSH Term	Description	Entries
D008297	Male		Males
D008875	Middle Aged	An adult aged 45 - 64 years.	Middle Age
D009902	Optic Neuritis	Inflammation of the optic nerve. Commonly associated conditions include autoimmune disorders such as MULTIPLE SCLEROSIS, infections, and granulomatous diseases. Clinical features include retro-orbital pain that is aggravated by eye movement, loss of color vision, and contrast sensitivity that may progress to severe visual loss, an afferent pupillary defect (Marcus-Gunn pupil), and in some instances optic disc hyperemia and swelling. Inflammation may occur in the portion of the nerve within the globe (neuropapillitis or anterior optic neuritis) or the portion behind the globe (retrobulbar neuritis or posterior optic neuritis).	Neuropapillitis,Retrobulbar Neuritis,Anterior Optic Neuritis,Posterior Optic Neuritis,Anterior Optic Neuritides,Neuritides, Anterior Optic,Neuritides, Optic,Neuritides, Posterior Optic,Neuritides, Retrobulbar,Neuritis, Anterior Optic,Neuritis, Optic,Neuritis, Posterior Optic,Neuritis, Retrobulbar,Neuropapillitides,Optic Neuritides,Optic Neuritides, Anterior,Optic Neuritides, Posterior,Optic Neuritis, Anterior,Optic Neuritis, Posterior,Posterior Optic Neuritides,Retrobulbar Neuritides
D003937	Diagnosis, Differential	Determination of which one of two or more diseases or conditions a patient is suffering from by systematically comparing and contrasting results of diagnostic measures.	Diagnoses, Differential,Differential Diagnoses,Differential Diagnosis
D005260	Female		Females
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000328	Adult	A person having attained full growth or maturity. Adults are of 19 through 44 years of age. For a person between 19 and 24 years of age, YOUNG ADULT is available.	Adults
D000368	Aged	A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available.	Elderly
D012189	Retrospective Studies	Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.	Retrospective Study,Studies, Retrospective,Study, Retrospective
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