Table 4.
Area Under the Receiver Operating Characteristic Curve (AROC), Sensitivity and Specificity Values of Different Machine Learning Classifiers Using Optical Coherence Tomography (OCT) or Standard Automated Perimetry (SAP) Alone, or in Combination for Glaucoma Diagnosis
| Study | Input Data | No. of Eyes/Images | ML Classifiers | AROC/Sensitivity and Specificity OCT Parameter Alone | AROC/Sensitivity and Specificity SAP Parameters Alone | AROC/Sensitivity and Specificity for Combined Parameters |
|---|---|---|---|---|---|---|
| Brigatti et al. (1996)77 | SAP indices (mean defect, corrected loss variance, and short-term fluctuation) and structural data (cup/disk ratio, rim area, cup volume, and nerve fiber layer height) | 185 glaucoma, 54 healthy | NN | 87% sensitivity and 56% specificity | 84% sensitivity and 86% specificity | 90% sensitivity and 84% specificity |
| Bowd et al. (2008)76 | RNFL thickness + SAP | 69 glaucoma, 156 healthy | RVM | 0.809 | 0.815 | 0.845 |
| SSMoG | 0.817 | 0.841 | 0.896 | |||
| Grewal et al. (2008)78 | Age, sex, myopia, intraocular pressure (IOP), optic nerve head, and retinal nerve fiber layer (RNFL), SAP and GDx parameters | 35 glaucoma, 30 glaucoma suspects, 35 healthy | ANN | Sensitivity of 93.3% at 80% specificity (normal versus glaucoma) | ||
| Bizios et al. (2011)75 | SAP and OCT | 135 glaucoma, 125 healthy | ANN | 0.970 | 0.945 | 0.978 |
| Sugimoto et al. (2013)80 | VF damage, age, gender, right or left eye, axial length, 237 different OCT measurements | 224 glaucoma, 69 healthy | RAN | m-RNFL (0.86), cp-RNFL (0.77), GCL + IPL (0.80), rim area (0.78) | 0.9 (all parameters) | |
| Silva et al. (2013)79 | SD-OCT parameters and global indices of SAP | 62 glaucoma, 48 healthy | Conventional | 0.574−0.813 | 0.828−0.915 | |
| BAG | 0.893 | |||||
| NB | 0.912 | |||||
| MLP | 0.845 | |||||
| RBF | 0.857 | |||||
| RAN | 0.933 | |||||
| ENS | 0.910 | |||||
| CTREE | 0.777 | |||||
| ADA | 0.932 | |||||
| SVMG | 0.913 | |||||
| SVML | 0.929 | |||||
| Kim et al. (2017)81 | Age, IOP, corneal thickness, RNFL, GHT, MD, PSD | 178 glaucoma, 164 healthy | C5.0 | 0.97 | ||
| RAN | 0.979 | |||||
| SVM | 0.97 | |||||
| KNN | 0.97 |
ANN, artificial neural network; MLC, machine learning classifier; RVM, relevance vector machine; BAG, bagging; NB, naïve Bayes; NN, neural network; MLP, multilayer perception; RBF, radial basis function; RAN, random forest; ENS, ensemble selection; CTREE, classification tree; ADA, AdaBoost M1; SVML, support vector machine linear; SVMG, support vector machine Gaussian; SSMoG, subspace mixture of Gaussians; KNN, k-nearest neighbor; SAP, standard automatic perimetry; GHT, Glaucoma Hemifield test.