PURPOSE: 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). METHODS: 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. RESULTS: 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). CONCLUSION: 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.
PURPOSE: 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). METHODS: Observational cross-sectional study. Sixty two glaucomapatients 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. RESULTS: 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). CONCLUSION: 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.
Authors: Hassan Muhammad; Thomas J Fuchs; Nicole De Cuir; Carlos G De Moraes; Dana M Blumberg; Jeffrey M Liebmann; Robert Ritch; Donald C Hood Journal: J Glaucoma Date: 2017-12 Impact factor: 2.503
Authors: Rachel C Kenney; Mengling Liu; Lisena Hasanaj; Binu Joseph; Abdullah Abu Al-Hassan; Lisanne J Balk; Raed Behbehani; Alexander Brandt; Peter A Calabresi; Elliot Frohman; Teresa C Frohman; Joachim Havla; Bernhard Hemmer; Hong Jiang; Benjamin Knier; Thomas Korn; Letizia Leocani; Elena Hernandez Martinez-Lapiscina; Athina Papadopoulou; Friedemann Paul; Axel Petzold; Marco Pisa; Pablo Villoslada; Hanna Zimmermann; Lorna E Thorpe; Hiroshi Ishikawa; Joel S Schuman; Gadi Wollstein; Yu Chen; Shiv Saidha; Steven Galetta; Laura J Balcer Journal: Neurology Date: 2022-06-28 Impact factor: 11.800
Authors: Leonardo Seidi Shigueoka; José Paulo Cabral de Vasconcellos; Rui Barroso Schimiti; Alexandre Soares Castro Reis; Gabriel Ozeas de Oliveira; Edson Satoshi Gomi; Jayme Augusto Rocha Vianna; Renato Dichetti Dos Reis Lisboa; Felipe Andrade Medeiros; Vital Paulino Costa Journal: PLoS One Date: 2018-12-05 Impact factor: 3.240