Table 2.
Area Under the Receiver Operating Characteristic Curve (AROC) Values of Different Machine Learning (ML) Classifiers Using OCT Imaging for Glaucoma Diagnosis
| Study | Input Data | No. of Eyes/Images | ML Classifiers | AROC | OCT Parameter with Best Diagnostic Accuracy | AROC | Significance Level (best ML Approach Versus Conventional) |
|---|---|---|---|---|---|---|---|
| Burgansky-Eliash Z. et al. (2005)27 | 38 conventional OCT parameters (macular and ONH) | 27 early glaucoma, 20 advanced glaucoma, 42 healthy eyes | LDA | 0.979 | Rim area | 0.969 | 0.07 |
| SVML | 0.981 | ||||||
| RPART | 0.885 | Mean RNFL | 0.938 | 0.01 | |||
| GLM | 0.975 | ||||||
| GAM | 0.854 | ||||||
| Huang et al. (2005)51 | 56 OCT parameters | 89 glaucoma, 100 healthy | LDA | 0.824 | Inferior quadrant thickness | 0.832 | n/a |
| MD | 0.849 | ||||||
| ANN | 0.821 | ||||||
| Naithani et al. (2007)52 | Peripapillary RNFL and ONH parameters (HRT) 19 parameters | 30 early glaucoma 30 moderate glaucoma 60 healthy | LDA | 0.982 | Average RNFL thickness | 0.953 | n/a |
| ANN | 0.938 | ||||||
| CTREE | 0.979 | ||||||
| Bizios et al. (2010)54 | 28 RNFL parameters | 62 glaucoma, 90 healthy | SVML | 0.959 to 0.999 | Global transformed A-scan data global transformed A-scan data | 0.977 0.977 | n/a |
| ANN | 0.958 to 0.995 | ||||||
| Barella et al. (2013)53 | 23 parameters (RNFL thickness and ONH topography) | 57 glaucoma, 46 healthy | SVML | 0.690 | Cup/disc area ratio | 0.846 | 0.542 |
| BAG | 0.804 | ||||||
| NB | 0.818 | ||||||
| SVMG | 0.753 | ||||||
| MLP | 0.768 | ||||||
| RBF | 0.839 | ||||||
| RAN | 0.877 | ||||||
| ENS | 0.793 | ||||||
| CTREE | 0.687 | ||||||
| ADA | 0.839 | ||||||
| Xu J et al. (2013)57 | OCT with super pixel analysis | 59 glaucoma suspects | Log | 0.903 | Average RNFL thickness | 0.707 | 0.031 |
| 84 glaucoma | |||||||
| 44 healthy | |||||||
| Larrosa et al. (2015)55 | RNFL thickness: 2 semi-circles, 4 quadrants, and 6, 8, 12, 16, 24, 32, 64, and 768 sectors | 117 glaucoma | ANN | 0.770 to 0.845 | 12 peripapillary RNFL thickness sectors | 0.845 | 0.0001 |
| 123 healthy | |||||||
| Muhammad et al. (2017)56 | RNFL thicknesses and retinal ganglion cell plus inner plexiform layer | 57 glaucoma, 45 healthy | RAN | 0.77 to 0.97 | Average RNFL thickness | 0.973 | n/a |
| Maetschke et al. (2019)58 | RNFL thicknesses, rim area, disc area, cup-to-disc ratio, vertical cup-to-disc ratio, cup volume | 263 healthy, 847 glaucoma | DL | 0.94 | n/a | n/a | n/a |
KNN, k-nearest neighbor; LDA, linear discriminant analysis; SVML, support vector machine linear; RPART, recursive partitioning and regression tree; GLM, generalized linear model; GAM, generalized additive model; MD, Mahalanobis distance; ANN, artificial neural network; Log, LogitBoost adaptive boosting; BAG, bagging; NB, naive-bayes; SVMG, support vector machine Gaussian; MLP, multi-layer perception; RBF, radial basis function; RAN, random forest; ENS, ensemble selections; CTREE, classification tree; ADA, AdaBoost M1; SAP, standard automatic perimetry.