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Table 5 Summary of studies using machine learning classifier for different KC or subclinical KC eyes from normal eyes

From: Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

Authors

Year

Instruments

ML classifier

Subjects

Results

Current Study

2019

UHR-OCT, Scheimpflug camera

Neural network

38 eyes with KC, 33 eyes with subclinical KC, 50 normal eyes

93% precision for subclinical KC eyes, 99% precision for KC eyes

Smolek et al. [9]

1997

Corneal topography

Neural network

6 KC suspect eyes, 33 eyes with KC

100% accuracy, sensitivity and specificity for all KC suspect and KC eyes

Accardo et al. [24]

2002

Corneal topography

Neural network

120 eyes with early KC eyes, 120 normal eyes

94.1% sensitivity, 97.6% specificity for early KC eyes

Arbelaez et al. [11]

2012

Scheimpflug camera and Placido corneal topography

SVM

877 eyes with KC, 426 eyes with subclinical KC, 1259 healthy control eyes

98.2% accuracy (95.0% sensitivity and 99.3% specificity) for KC eyes and 97.3% accuracy (92.0% sensitivity and 97.7% specificity) for subclinical KC eyes

Smadja et al. [10]

2013

Scheimpflug camera

Decision tree

148 eyes with KC, 177 eyes with forme fruste KC, 372 healthy control eyes

100% sensitivity and 99.5% specificity for KC eyes, 93.6% sensitivity and 97.2% specificity for forme fruste KC eyes

Kovacs et al. [25]

2016

Scheimpflug camera

Neural network

60 eyes with KC, 15 eyes with preclinical KC, 60 healthy control eyes

0.99 AUC, 100% sensitivity and 98% specificity for KC eyes, 0.96 AUC, 92% sensitivity and 85% specificity for preclinical KC eyes

Saad et al. [26]

2016

Placido based corneal topography and corneal wavefront measurements

Neural network

62 eyes with forme fruste KC, 114 normal eyes

0.97 AUC, 63% sensitivity and 82% for forme fruste KC, 100% sensitivity and 82% specificity for KC eyes

Hidalgo et al. [27]

2016

Scheimpflug camera

SVM

454 eyes with KC, 67 eyes with forme fruste KC, 194 normal eyes

98.9% accuracy, 99.1% sensitivity and 98.5% specificity for KC eyes, 93.1% accuracy, 79.1% sensitivity and 97.7% specificity for forme fruste KC eyes

Ambrosio et al. [21]

2017

Scheimpflug camera and biomechanical camera

SVM, random forest

111 eyes with KC, 227 normal eyes

1.0 AUC for KC eyes

Lopes et al. [12]

2018

Scheimpflug camera

Random forest

71 eyes with ectasia susceptibility, 182 eyes with KC, 2980 normal eyes

85.2% sensitivity and 0.966 specificity, 0.968 AUC for suspected KC eyes.

Issarti et al. [28]

2019

Scheimpflug camera

Neural network

77 eyes with suspect KC, 312 normal eyes

96.56% accuracy, 97.78% sensitivity and 95.56% specificity for suspect KC eyes

  1. KC= keratoconus; UHR-OCT= ultra-high-resolution optical coherence tomography; ML= machine learningÂ