Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry.
OBJECTIVE
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).


CONCLUSIONS
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.
Glaucoma Service, Department of Ophthalmology, Universidade Estadual de Campinas, Campinas (SP), Brazil. silvafr@bol.com.br
Fabrício R
FR
Silva
Vanessa G
VG
Vidotti
Fernanda
F
Cremasco
Marcelo
M
Dias
Edson S
ES
Gomi
Vital P
VP
Costa
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D016022:Case-Control Studies; D016009:Chi-Square Distribution; D003430:Cross-Sectional Studies; D005260:Female; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence / Q000295:instrumentation* / Q000379:methods; D058609:Visual Field Tests / Q000295:instrumentation* / Q000379:methods; D014794:Visual Fields