Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/200773
Type: Artigo de periódico
Title: Sensitivity And Specificity Of Machine Learning Classifiers For Glaucoma Diagnosis Using Spectral Domain Oct And Standard Automated Perimetry.
Author: Silva, Fabrício R
Vidotti, Vanessa G
Cremasco, Fernanda
Dias, Marcelo
Gomi, Edson S
Costa, Vital P
Abstract: To 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.
Subject: Adult
Aged
Aged, 80 And Over
Artificial Intelligence
Case-control Studies
Chi-square Distribution
Cross-sectional Studies
Female
Glaucoma
Humans
Male
Middle Aged
Roc Curve
Reference Values
Reproducibility Of Results
Sensitivity And Specificity
Tomography, Optical Coherence
Visual Field Tests
Visual Fields
Rights: aberto
Identifier DOI: 
Address: http://www.ncbi.nlm.nih.gov/pubmed/23929078
Date Issue: -1-Uns- -1
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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