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dc.contributor.authorClark, Patrick G.
dc.contributor.authorAgah, Arvin
dc.contributor.authorCibis, Gerhard W.
dc.date.accessioned2016-01-29T16:24:28Z
dc.date.available2016-01-29T16:24:28Z
dc.date.issued2011
dc.identifier.citationClark, Patrick G., Arvin Agah, and Gerhard W. Cibis. "Applied Artificial Intelligence Techniques for Identifying the Lazy Eye Vision Disorder." Journal of Intelligent Systems 20.2 (2011): n. pag. http://dx.doi.org/10.1515/JISYS.2011.007.en_US
dc.identifier.urihttp://hdl.handle.net/1808/19809
dc.descriptionThis is the published version. Copyright De Gruyteren_US
dc.description.abstractAmblyopia, or lazy eye, is a neurological vision disorder that studies have shown to affect two to five percent of the population. Current methods of treatment produce the best visual outcome, if the condition is identified early in the patient's life. Several early screening procedures are aimed at finding the condition while the patient is a child, including an automated vision screening system. This paper aims to use artificial intelligence techniques to automatically identify children who are at risk for developing the amblyopic condition and should therefore be referred to a specialist, i.e., pediatric ophthalmologist. Three techniques, namely, decision tree learning, random forest, and artificial neural network, are studied in this paper in terms of their effectiveness, using metrics of sensitivity, specificity, and accuracy. The features used by the techniques are extracted from images of patient eyes and are based on the color information. The efficacy of pixel color data is investigated with respect to the measurement of the rate of change of the color in the iris and pupil, i.e., color slope features. A 10-fold stratified cross validation procedure is used to compare the effectiveness of the three AI techniques in this medical application domain.en_US
dc.publisherDe Gruyteren_US
dc.subjectApplied artificial intelligenceen_US
dc.subjectPattern analysisen_US
dc.subjectDecision treeen_US
dc.subjectRandom foresten_US
dc.subjectArtificial neural networken_US
dc.subjectLazy eyeen_US
dc.subjectAmblyopiaen_US
dc.titleApplied Artificial Intelligence Techniques for Identifying the Lazy Eye Vision Disorderen_US
dc.typeArticle
kusw.kuauthorAgah, Arvin
kusw.kudepartmentEngineering Administrationen_US
dc.identifier.doi10.1515/JISYS.2011.007
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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