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    Applied Artificial Intelligence Techniques for Identifying the Lazy Eye Vision Disorder

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    Issue Date
    2011
    Author
    Clark, Patrick G.
    Agah, Arvin
    Cibis, Gerhard W.
    Publisher
    De Gruyter
    Type
    Article
    Article Version
    Scholarly/refereed, publisher version
    Metadata
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    Abstract
    Amblyopia, 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.
    Description
    This is the published version. Copyright De Gruyter
    URI
    http://hdl.handle.net/1808/19809
    DOI
    https://doi.org/10.1515/JISYS.2011.007
    Collections
    • Electrical Engineering and Computer Science Scholarly Works [301]
    Citation
    Clark, 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.

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    KU Libraries
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    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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