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Integration and fusion of standard automated perimetry and optical coherence tomography data for improved automated glaucoma diagnostics.Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurementsEvaluation of machine learning classifiers in keratoconus detection from orbscan II examinations.Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field pointsPrediction accuracy of a novel dynamic structure-function model for glaucoma progression.Improving glaucoma detection using spatially correspondent clusters of damage and by combining standard automated perimetry and optical coherence tomographyMachine Learning Techniques in Clinical Vision Sciences.The Future of Imaging in Detecting Glaucoma Progression.
P2860
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P2860
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on June 2008
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Machine learning classifiers in glaucoma.
@en
Machine learning classifiers in glaucoma.
@nl
type
label
Machine learning classifiers in glaucoma.
@en
Machine learning classifiers in glaucoma.
@nl
prefLabel
Machine learning classifiers in glaucoma.
@en
Machine learning classifiers in glaucoma.
@nl
P1476
Machine learning classifiers in glaucoma
@en
P2093
Michael H Goldbaum
P304
P356
10.1097/OPX.0B013E3181783AB6
P577
2008-06-01T00:00:00Z