Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements
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چکیده
منابع مشابه
Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.
PURPOSE To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). METHODS Seventy-two eyes of 72 healthy control subjects (average age = 64.3 +/- 8.8 years, visual field mean deviation = -0.71 +/- 1.2 dB) an...
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PURPOSE To determine the correlations and strength of association between different imaging systems in analyzing the retinal nerve fiber layer (RNFL) of glaucoma patients: optical coherence tomography (OCT), scanning laser polarimetry (SLP) and confocal scanning laser ophthalmoscopy (CSLO). MATERIALS AND METHODOLOGY 114 eyes of patients with moderate open angle glaucoma underwent spectral dom...
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purpose: to quantify the intraobserver reproducibility of retinal nerve fiber layer (rnfl) measurements using scanning laser polarimetry with variable corneal compensation in glaucoma suspect patients. methods: twenty-six eyes of 26 glaucoma suspect patients were included. complete ophthalmologic examination and standard automated perimetry were performed for all of them. rnfl measurements were...
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PURPOSE To compare the ability of scanning laser polarimetry with variable corneal compensation (GDx-VCC) and Stratus optical coherence tomography (OCT) to detect photographic retinal nerve fiber layer (RNFL) defects. METHODS This retrospective cross-sectional study included 45 eyes of 45 consecutive glaucoma patients with RNFL defects in red-free fundus photographs. The superior and inferior...
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ژورنال
عنوان ژورنال: Investigative Opthalmology & Visual Science
سال: 2005
ISSN: 1552-5783
DOI: 10.1167/iovs.04-1122