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Title
Japanese: 
English:Automatic screening of narrow anterior chamber angle and angle-closure glaucoma based on slit-lamp image analysis by using support vector machine 
Author
Japanese: Chonlada Theeraworn, Waree Kongprawechnon, Toshiaki Kondo, Pished Bunnun, 西原明法, Anita Manassakorn.  
English: Chonlada Theeraworn, Waree Kongprawechnon, Toshiaki Kondo, Pished Bunnun, AKINORI NISHIHARA, Anita Manassakorn.  
Language English 
Journal/Book name
Japanese: 
English: 
Volume, Number, Page        
Published date July 2013 
Publisher
Japanese: 
English:IEEE 
Conference name
Japanese: 
English:35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 
Conference site
Japanese: 
English:Osaka 
DOI https://doi.org/10.1109/EMBC.2013.6610891
Abstract At present, Van Herick's method is a standard technique used to screen a Narrow Anterior Chamber Angle (NACA) and Angle-Closure Glaucoma (ACG). It can identify a patient who suffers from NACA and ACG by considering the width of peripheral anterior chamber depth (PACD) and corneal thickness. However, the screening result of this method often varies among ophthalmologists. So, an automatic screening of NACA and ACG based on slit-lamp image analysis by using Support Vector Machine (SVM) is proposed. SVM can automatically generate the classification model, which is used to classify the result as an angle-closure likely or an angle-closure unlikely. It shows that it can improve the accuracy of the screening result. To develop the classification model, the width of PACD and corneal thickness from many positions are measured and selected to be features. A statistic analysis is also used in the PACD and corneal thickness estimation in order to reduce the error from reflection on the cornea. In this study, it is found that the generated models are evaluated by using 5-fold cross validation and give a better result than the result classified by Van Herick's method.

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