Glaucoma Classification Model Based on GDx VCC Measured Parameters by Decision Tree

This study is to propose and evaluate the diagnostic accuracy of decision tree classifiers using the full set of standard GDx VCC measurements for classifying glaucoma in a Taiwan Chinese population. The classifiers were trained and tested using standard GDx VCC parameters from examinations of 74 subjects with glaucoma and 72 normal subjects. Six promising decision rules were generated from decision tree methods and the overall accuracy from tenfold cross validation was 0.801. Classification tree based on GDx VCC data promises to be a diagnostic tool in glaucoma disease. However, its exact clinical application in glaucoma practice should be retested. Further longitudinal study should address this issue.

[1]  Xiang-Run Huang,et al.  Linear birefringence of the retinal nerve fiber layer measured in vitro with a multispectral imaging micropolarimeter. , 2002, Journal of biomedical optics.

[2]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[3]  Andreas W. Dreher,et al.  Assessment of the retinal nerve fiber layer by scanning-laser polarimetry , 1993, Photonics West - Lasers and Applications in Science and Engineering.

[4]  J. Jonas,et al.  Morphometry of the human lamina cribrosa surface. , 1991, Investigative ophthalmology & visual science.

[5]  Mei-Ling Huang,et al.  Diagnostic Value of GDx Polarimetry in a Taiwan Chinese Population , 2007, Optometry and vision science : official publication of the American Academy of Optometry.

[6]  Hans G Lemij,et al.  Diagnostic accuracy of the GDx VCC for glaucoma. , 2004, Ophthalmology.

[7]  H A Quigley,et al.  Regional differences in the structure of the lamina cribrosa and their relation to glaucomatous optic nerve damage. , 1981, Archives of ophthalmology.

[8]  A M Vossepoel,et al.  Automated detection of wedge-shaped defects in polarimetric images of the retinal nerve fibre layer , 2006, Eye.

[9]  Giuseppe Di Stefano,et al.  GDx-VCC performance in discriminating normal from glaucomatous eyes with early visual field loss , 2006, Graefe's Archive for Clinical and Experimental Ophthalmology.

[10]  Robert N Weinreb,et al.  Is scanning laser polarimetry ready for clinical practice? , 2007, American journal of ophthalmology.

[11]  F. Medeiros,et al.  The effects of study design and spectrum bias on the evaluation of diagnostic accuracy of confocal scanning laser ophthalmoscopy in glaucoma. , 2007, Investigative ophthalmology & visual science.

[12]  A. Abu-Hanna,et al.  Identification of high-risk subgroups in very elderly intensive care unit patients , 2007, Critical care.

[13]  R. Pandey,et al.  Evaluation of optical coherence tomography and heidelberg retinal tomography parameters in detecting early and moderate glaucoma. , 2007, Investigative ophthalmology & visual science.

[14]  R. Weinreb,et al.  Histopathologic validation of Fourier-ellipsometry measurements of retinal nerve fiber layer thickness. , 1990, Archives of ophthalmology.

[15]  J Caprioli,et al.  Slope of the peripapillary nerve fiber layer surface in glaucoma. , 1998, Investigative ophthalmology & visual science.

[16]  A. Abu-Hanna,et al.  Prognostic Models in Medicine , 2001, Methods of Information in Medicine.

[17]  Joseph Caprioli,et al.  Correction for the erroneous compensation of anterior segment birefringence with the scanning laser polarimeter for glaucoma diagnosis. , 2002, Investigative ophthalmology & visual science.

[18]  Robert N Weinreb,et al.  Scanning Laser Polarimetry in Monkey Eyes using Variable Corneal Polarization Compensation , 2002, Journal of glaucoma.

[19]  R. Thomas,et al.  Retinal nerve fibre layer imaging compared with histological measurements in a human eye , 2009, Eye.

[20]  H. Lemij,et al.  The value of polarimetry in the evaluation of the optic nerve in glaucoma , 2001, Current opinion in ophthalmology.

[21]  Barry Cense,et al.  Thickness and birefringence of healthy retinal nerve fiber layer tissue measured with polarization-sensitive optical coherence tomography. , 2004, Investigative ophthalmology & visual science.

[22]  F. Medeiros,et al.  Comparison of the GDx VCC scanning laser polarimeter, HRT II confocal scanning laser ophthalmoscope, and stratus OCT optical coherence tomograph for the detection of glaucoma. , 2004, Archives of ophthalmology.

[23]  D. Garway-Heath,et al.  Sources of bias in studies of optic disc and retinal nerve fibre layer morphology , 1998, The British journal of ophthalmology.

[24]  Torsten Hothorn,et al.  Bagging Tree Classifiers for Laser Scanning Images: Data and Simulation Based Strategy , 2002, Artif. Intell. Medicine.

[25]  L. Zangwill,et al.  Glaucoma detection using scanning laser polarimetry with variable corneal polarization compensation. , 2003, Archives of ophthalmology.

[26]  Chris A Johnson,et al.  Classification of visual field abnormalities in the ocular hypertension treatment study. , 2000, Archives of ophthalmology.

[27]  T. Sejnowski,et al.  Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements. , 2005, Investigative ophthalmology & visual science.

[28]  J. Jonas,et al.  Ophthalmoscopic evaluation of the optic nerve head. , 1999, Survey of ophthalmology.

[29]  R. Weinreb,et al.  Individualized compensation of anterior segment birefringence during scanning laser polarimetry. , 2002, Investigative ophthalmology & visual science.

[30]  Nicolette de Keizer,et al.  Integrating classification trees with local logistic regression in Intensive Care prognosis , 2003, Artif. Intell. Medicine.

[31]  Christopher Kai-shun Leung,et al.  Comparative study of retinal nerve fiber layer measurement by StratusOCT and GDx VCC, I: correlation analysis in glaucoma. , 2005, Investigative ophthalmology & visual science.

[32]  Mei-Ling Huang,et al.  Rule extraction for glaucoma detection with summary data from StratusOCT. , 2007, Investigative ophthalmology & visual science.

[33]  J. Hanley,et al.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.