1. Classification Algorithms Enhance the Discrimination of Glaucoma from Normal eyes in High Definition Optical Coherence Tomography

    Classification Algorithms Enhance the Discrimination of Glaucoma from Normal eyes in High Definition Optical Coherence Tomography

    Purpose: To evaluate the diagnostic performance of classification algorithms based on Linear Discriminant Analysis (LDA) and Classification And Regression Tree (CART) methods, compared to optic nerve head (ONH) and retinal nerve fiber layer (RNFL) parameters measured by high-definition optical coherence tomography (Cirrus HD-OCT Version 4.5.1.1, Carl Zeiss Meditec Inc., CA) for discriminating glaucoma subjects. Methods: Consecutive glaucoma subjects (Training data=184; Validation data=102) were recruited from an eye center and normal subjects (n=508) from an ongoing Singaporean Chinese population based study. ONH and RNFL parameters were measured using Optic Disc Cube 200x200 scan protocol. LDA ...

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