Sensitivity And Specificity Of Machine Learning Classifiers For Glaucoma Diagnosis Using Spectral Domain Oct And Standard Automated Perimetry.

dc.creatorSilva, Fabrício R
dc.creatorVidotti, Vanessa G
dc.creatorCremasco, Fernanda
dc.creatorDias, Marcelo
dc.creatorGomi, Edson S
dc.creatorCosta, Vital P
dc.date
dc.date2015-11-27T13:32:04Z
dc.date2015-11-27T13:32:04Z
dc.date.accessioned2018-03-29T01:18:20Z
dc.date.available2018-03-29T01:18:20Z
dc.identifierArquivos Brasileiros De Oftalmologia. v. 76, n. 3, p. 170-4
dc.identifier1678-2925
dc.identifier
dc.identifierhttp://www.ncbi.nlm.nih.gov/pubmed/23929078
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/200773
dc.identifier23929078
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1301006
dc.descriptionTo evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.
dc.description76
dc.description170-4
dc.languageeng
dc.relationArquivos Brasileiros De Oftalmologia
dc.relationArq Bras Oftalmol
dc.rightsaberto
dc.rights
dc.sourcePubMed
dc.subjectAdult
dc.subjectAged
dc.subjectAged, 80 And Over
dc.subjectArtificial Intelligence
dc.subjectCase-control Studies
dc.subjectChi-square Distribution
dc.subjectCross-sectional Studies
dc.subjectFemale
dc.subjectGlaucoma
dc.subjectHumans
dc.subjectMale
dc.subjectMiddle Aged
dc.subjectRoc Curve
dc.subjectReference Values
dc.subjectReproducibility Of Results
dc.subjectSensitivity And Specificity
dc.subjectTomography, Optical Coherence
dc.subjectVisual Field Tests
dc.subjectVisual Fields
dc.titleSensitivity And Specificity Of Machine Learning Classifiers For Glaucoma Diagnosis Using Spectral Domain Oct And Standard Automated Perimetry.
dc.typeArtículos de revistas


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