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Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan

4 Issues per year


CiteScore 2016: 0.39

SCImago Journal Rank (SJR) 2015: 0.189
Source Normalized Impact per Paper (SNIP) 2015: 0.593

Online
ISSN
2191-026X
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Volume 20, Issue 2 (Jan 2011)

Issues

Applied Artificial Intelligence Techniques for Identifying the Lazy Eye Vision Disorder

Patrick G. Clark
  • Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, KS 66045, USA.
/ Arvin Agah
  • Corresponding author
  • Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, KS 66045, USA.
  • Email:
/ Gerhard W. Cibis
  • Cibis Eye Care, Kansas City, MO 64112, USA.
Published Online: 2011-06-18 | DOI: https://doi.org/10.1515/jisys.2011.007

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.

Keywords.: Applied artificial intelligence; pattern analysis; decision tree; random forest; artificial neural network; lazy eye; amblyopia

About the article

Received: 2011-04-10

Published Online: 2011-06-18

Published in Print: 2011-08-01



Citation Information: Journal of Intelligent Systems, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys.2011.007. Export Citation

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