April 2010
Volume 51, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2010
Sensitivity and Specificity of Machine Learning Classifiers and Spectral Domain OCT for the Diagnosis of Glaucoma
Author Affiliations & Notes
  • V. P. Costa
    Ophthalmology, University of Campinas, Sao Paulo, Brazil
  • V. G. Vidotti
    Ophthalmology, University of Campinas, Sao Paulo, Brazil
  • G. M. Resende
    Ophthalmology, University of Campinas, Sao Paulo, Brazil
  • F. R. Silva
    Ophthalmology, University of Campinas, Sao Paulo, Brazil
  • F. Cremasco
    Ophthalmology, University of Campinas, Sao Paulo, Brazil
  • M. Dias
    Engineering, University of São Paulo, Sao Paulo, Brazil
  • E. Gomi
    Engineering, University of São Paulo, Sao Paulo, Brazil
  • Footnotes
    Commercial Relationships  V.P. Costa, None; V.G. Vidotti, None; G.M. Resende, None; F.R. Silva, None; F. Cremasco, None; M. Dias, None; E. Gomi, None.
  • Footnotes
    Support  FAPESP 07/51281-9
Investigative Ophthalmology & Visual Science April 2010, Vol.51, 227. doi:
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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)

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Abstract

Purpose: : To investigate the sensitivity and specificity of machine learning classifiers and Spectral Domain OCT (SD-OCT) for the diagnosis of glaucoma.

Methods: : 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.

Results: : 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).

Conclusions: : In this series, machine learning classifiers did not improve the sensitivity and specificity of SD-OCT for the diagnosis of glaucoma.

Keywords: imaging/image analysis: clinical • nerve fiber layer 
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