Table 3.
Summary of Studies Using Machine Learning (ML) Classifiers to Detect Glaucoma From Perimetric Datasets
| Study | Input Data | No. of Eyes/Images | ML Classifiers | Significance Level |
|---|---|---|---|---|
| Goldbaum et al. (1994)63 | Central 24° of standard automated perimetry with Humphrey Visual Field 24-2 or 30-2 SITA Standard visual field test | 120 eyes, 60 normal 60 glaucomatous | Trained two layered ANN | Experts versus two-layered neural network. Sensitivity: 59% vs. 65% Specificity: 74% vs 71% Agreement 74% |
| Goldbaum et al. (2002)68 | SAP Humphrey visual field 24-2 or 30-2 | 189 normal eyes and 156 glaucomatous eyes | MLP, SVM, MoG, MGG | AROC 0.922, sensitivity 79%, specificity 90% |
| Chan et al. (2002)67 | SAP | 189 normal eyes and 156 glaucomatous eyes | MLP, SVM, LDA, QDA, Parzen window, MOG, MGG | AROC 0.88-0.92 sensitivity 58.3−78.2% specificity 90% |
| Sample et al. (2004)64 | Standard automated perimetry with Humphrey visual field 24-2 or 30-2 SITA standard visual field test | 345 eyes, 189 normal | vbMFA (unsupervised) | Comparing clusters versus GHT = 0.913−0.875 versus PSD = 0.905−0.863 versus expert = 0.873−0.829 |
| Bizios et al. (2007)70 | Standard automated perimetry with Humphrey visual field 30-2 | 100 glaucoma eyes, 116 normal eyes | Trained artificial neural network compared to PSD | ANN: AROC 0.984, sensitivity 93%, specificity 94% PSD (<5%): sensitivity 89%, specificity 93% PSD (<1%): sensitivity 72%, specificity 97% |
| Andersson et al. (2013)59 | Standard automated perimetry with Humphrey visual field 30-2 SITA standard visual field test | 99 glaucoma patients, 66 healthy subjects | Trained artificial neural network | 30 physicians (varying experience) versus trained artificial neural network Sensitivity: 83% vs. 93% Specificity: 90% vs. 91% |
| Bowd et al. (2014)61 | FDT perimetry with Humphrey matrix (24-2 test pattern) | 1976 eyes FDT normal 1190 FDT abnormal 786 | Variational Bayesian independent component analysis-mixture model | compared to FDT sensitivity: 82.8% specificity: 93.1% |
| Asaoka et al. (2016)60 | Standard automated perimetry with Humphrey visual field 30-2 SITA standard visual field test | 108 healthy eyes, 171 pre- perimetric glaucoma eyes | Deep FNN RF NN | AROC: Deep FNN 92.6% RF 77.6% NN 66.7% |
| Cai et al. (2017)62 | Standard automated perimetry with Humphrey visual field 24-2 SITA standard visual field test | 243 eyes mean MD −11.0 ± 8.7dB and PSD 9.5 ± 4.1dB | Archetypal analysis (unsupervised) | AT2 (superior defect) and ptosis P < 0.001 AT12 cluster and stroke presence (temporal hemianopia) P = 0.02 AT1 (no focal defect) and GHT within normal limits P < 0.001 |
| Li et al. (2018)71 | Standard automated perimetry with Humphrey visual field 24-2 and 30-2 SITA standard visual field test | 1623 normal eyes and 87 glaucomatous eyes (early stage) | DL | Sensitivity 93.2%, specificity 82.6% |
| Kucur et al. (2018)72 | OCTOPUS 101 G1 program and the Humphrey Field Analyzer 24–2 | 158 normal eyes and 307 glaucomatous eyes | DL | Average precision 87.40% |
ANN, artificial neural network; MD, mean deviation; GHT, Glaucoma Hemifield test; PSD, pattern standard deviation; FDT, frequency doubling technology; AROC, area under the receiver operating characteristic curve; vbMFA, variational Bayesian mixture of factor analysis; FNN, feed-forward neural network; RF, random forests; NN, neural network; MLP, multilayer perception; SVM, support vector machines; MoG, mixture of Gaussian; MGG, mixture of generalized Gaussian classifiers; LDA, linear discriminant analysis; QDA, quadratic discriminant analysis; SAP, standard automated perimetry; DL, deep learning.