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Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study
2005
Investigative Ophthalmology and Visual Science
PURPOSE. Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. METHODS. Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual
doi:10.1167/iovs.05-0366
pmid:16249492
pmcid:PMC1941765
fatcat:d672aqxuh5fl7h4rdrvtbmujra