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V. P. Costa, V. G. Vidotti, G. M. Resende, F. R. Silva, F. Cremasco, M. Dias, E. Gomi; Sensitivity and Specificity of Machine Learning Classifiers and Spectral Domain OCT for the Diagnosis of Glaucoma. Invest. Ophthalmol. Vis. Sci. 2010;51(13):227.
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© ARVO (1962-2015); The Authors (2016-present)
To investigate the sensitivity and specificity of machine learning classifiers and Spectral Domain OCT (SD-OCT) for the diagnosis of glaucoma.
Sixty six glaucoma patients (POAG) and 43 healthy individuals were recruited at the Glaucoma Service of the University of Campinas, Brazil. Inclusion criteria for glaucoma patients were: POAG, IOP > 21mmHg, best-corrected visual acuity of 20/40 or better, spherical equivalent within 5.0 D, ≥ 40 years of age, 2 consecutive abnormal and reliable visual fields. Inclusion criteria for healthy individuals were : IOP < 21 mmHg, best-corrected visual acuity of 20/40 or better, spherical equivalent within 5.0 D, ≥ 40 years of age, 2 consecutive and reliable normal visual fields, open angle on gonioscopy. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP Humphrey 24-2 SITA, Carl Zeiss Meditec, Inc, Dublin, California) and RNFL imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). SD-OCT images were obtained with undilated pupils by the same observer (VGV), who was masked for the diagnosis. ROC curves were obtained for all SD-OCT parameters. Subsequently, the following machine learning classifiers were tested: BAG (Bagging), NB (Naive-Bayes), MLP (Multiple Layer Program), and CTR (Decision Trees) algorithms. Areas under the ROC curves (aROCs) obtained for each parameter and each machine learning classifier were compared.
The mean age was 48.5 ± 9.1 years for healthy individuals and 61.1 ± 10.3 years for glaucoma patients (p<0.001). There was no significant difference between the control and glaucoma groups regarding intraocular pressure (IOP) (13.6 ± 2.8 mmHg and 13.8 ± 2.7 mmHg, respectively) (p=0.42), but glaucoma patients were using a mean number of 2.2 ± 1.1 medications to lower IOP. MD values were -6.2 ± 6.1 dB for glaucoma patients and -1.5 ± 1.5 dB for healthy individuals (p<0.001). SD-OCT parameters with the greater aROCs were: average thickness (0.810 - CI 0.717-0.883), inferior quadrant (0.801- CI 0.708-0.876), 7 o'clock position (0.815 - CI 0.723-0,887) and 11 o'clock position (0.852 - CI 0.765-0.916). aROCs with machine learning classifiers varied from 0.585 (CTR) to 0.829 (BAG). aROC obtained with BAG was not significantly different from the aROC obtained with the best SD-OCT parameter (p= 0.61).
In this series, machine learning classifiers did not improve the sensitivity and specificity of SD-OCT for the diagnosis of glaucoma.
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