Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements.
暂无分享,去创建一个
Robert N Weinreb | Christopher Bowd | Intae Lee | Christopher A Girkin | Linda M Zangwill | Michael H Goldbaum | Felipe A Medeiros | Madhusudhanan Balasubramanian | Jeffrey M Liebmann | M. Goldbaum | F. Medeiros | L. Zangwill | Intae Lee | R. Weinreb | J. Liebmann | C. Bowd | C. Girkin | M. Balasubramanian
[1] Mei-Ling Huang,et al. Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography. , 2005, Investigative ophthalmology & visual science.
[2] Anders Heijl,et al. Effects of input data on the performance of a neural network in distinguishing normal and glaucomatous visual fields. , 2005, Investigative ophthalmology & visual science.
[3] S. Miglior,et al. Predictive factors for open-angle glaucoma among patients with ocular hypertension in the European Glaucoma Prevention Study. , 2007, Ophthalmology.
[4] Terrence J. Sejnowski,et al. Comparison of machine learning and traditional classifiers in glaucoma diagnosis , 2002, IEEE Transactions on Biomedical Engineering.
[5] Anders Heijl,et al. Trained Artificial Neural Network for Glaucoma Diagnosis Using Visual Field Data: A Comparison With Conventional Algorithms , 2007, Journal of glaucoma.
[6] Robert N Weinreb,et al. Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc. , 2002, Investigative ophthalmology & visual science.
[7] Christopher Bowd,et al. Machine Learning Classifiers in Glaucoma , 2008, Optometry and vision science : official publication of the American Academy of Optometry.
[8] Robert N Weinreb,et al. Combining Functional and Structural Tests Improves the Diagnostic Accuracy of Relevance Vector Machine Classifiers , 2010, Journal of glaucoma.
[9] Edward A Essock,et al. Predicting Visual Field Loss in Ocular Hypertensive Patients Using Wavelet-Fourier Analysis of GDx Scanning Laser Polarimetry , 2004, Optometry and vision science : official publication of the American Academy of Optometry.
[10] S. Fieuws,et al. Agreement and accuracy of non-expert ophthalmologists in assessing glaucomatous changes in serial stereo optic disc photographs. , 2011, Ophthalmology.
[11] Robert N Weinreb,et al. Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes. , 2004, Investigative ophthalmology & visual science.
[12] C. Erb,et al. Short wavelength automated perimetry, frequency doubling technology perimetry, and pattern electroretinography for prediction of progressive glaucomatous standard visual field defects. , 2002, Ophthalmology.
[13] Susan Vitale,et al. Agreement among glaucoma specialists in assessing progressive disc changes from photographs in open-angle glaucoma patients. , 2009, American journal of ophthalmology.
[14] Christopher M. Bishop,et al. Variational Relevance Vector Machines , 2000, UAI.
[15] Anders Heijl,et al. Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT , 2010, Acta ophthalmologica.
[16] J. Jonas,et al. Predictive factors of the optic nerve head for development or progression of glaucomatous visual field loss. , 2004, Investigative ophthalmology & visual science.
[17] Robert N Weinreb,et al. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. , 2002, Investigative ophthalmology & visual science.
[18] M. Morales i Ballús,et al. Baseline Optical Coherence Tomography Predicts the Development of Glaucomatous Change in Glaucoma Suspects , 2007 .
[19] Susan E. George,et al. Artificial neural network analysis of noisy visual field data in glaucoma , 1997, Artif. Intell. Medicine.
[20] 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.
[21] George Eastman House,et al. Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .
[22] A Heijl,et al. Early Manifest Glaucoma Trial: design and baseline data. , 1999, Ophthalmology.
[23] D. Garway-Heath,et al. Predicting Progression to Glaucoma in Ocular Hypertensive Patients , 2009, Journal of glaucoma.
[24] Gang Li,et al. A Unified Approach to Nonparametric Comparison of Receiver Operating Characteristic Curves for Longitudinal and Clustered Data , 2008, Journal of the American Statistical Association.
[25] Robert N Weinreb,et al. Performance of confocal scanning laser tomograph Topographic Change Analysis (TCA) for assessing glaucomatous progression. , 2009, Investigative ophthalmology & visual science.
[26] F. Medeiros,et al. Prediction of functional loss in glaucoma from progressive optic disc damage. , 2009, Archives of ophthalmology.
[27] J. Beiser,et al. Baseline topographic optic disc measurements are associated with the development of primary open-angle glaucoma: the Confocal Scanning Laser Ophthalmoscopy Ancillary Study to the Ocular Hypertension Treatment Study. , 2005, Archives of ophthalmology.
[28] T. Sejnowski,et al. Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers. , 2004, Investigative ophthalmology & visual science.
[29] V Rihani,et al. Artificial Neural Network-Based Glaucoma Diagnosis Using Retinal Nerve Fiber Layer Analysis , 2008, European journal of ophthalmology.
[30] Chris A. Johnson,et al. The Ocular Hypertension Treatment Study: baseline factors that predict the onset of primary open-angle glaucoma. , 2002, Archives of ophthalmology.
[31] Robert N Weinreb,et al. Retinal nerve fiber layer thickness measurements with scanning laser polarimetry predict glaucomatous visual field loss. , 2004, American journal of ophthalmology.
[32] C. Glymour,et al. Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study. , 2005, Investigative ophthalmology & visual science.
[33] J Katz,et al. Neural networks for visual field analysis: how do they compare with other algorithms? , 1999, Journal of glaucoma.
[34] Te-Won Lee,et al. Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes. , 2008, Investigative ophthalmology & visual science.
[35] D B Henson,et al. Spatial classification of glaucomatous visual field loss. , 1996, The British journal of ophthalmology.
[36] Johan A. K. Suykens,et al. Advances in learning theory : methods, models and applications , 2003 .
[37] Jean L Freeman,et al. A non-parametric method for the comparison of partial areas under ROC curves and its application to large health care data sets. , 2002, Statistics in medicine.
[38] David Keating,et al. Visual field interpretation with a personal computer based neural network , 1994, Eye.
[39] Chris A Johnson,et al. Predicting progressive glaucomatous optic neuropathy using baseline standard automated perimetry data. , 2009, Investigative ophthalmology & visual science.
[40] M H Goldbaum,et al. Interpretation of automated perimetry for glaucoma by neural network. , 1994, Investigative ophthalmology & visual science.
[41] Terrence J. Sejnowski,et al. Probability of Glaucoma Determined from Standard Automated Perimetry and from Optic Disk Topography using Relevance Vector Machine Classifiers , 2004 .
[42] C. Micchelli,et al. Bayesian Regression and Classification , 2003 .
[43] C. Bunce,et al. The ability of the GDx Nerve Fibre Analyser neural network to diagnose glaucoma , 2001, Graefe's Archive for Clinical and Experimental Ophthalmology.
[44] L Brigatti,et al. Neural networks to identify glaucoma with structural and functional measurements. , 1996, American journal of ophthalmology.
[45] D B Henson,et al. Visual field analysis using artificial neural networks , 1994, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.
[46] K. A. Townsend,et al. Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection , 2008, British Journal of Ophthalmology.
[47] Robert N Weinreb,et al. Comparison of HRT-3 glaucoma probability score and subjective stereophotograph assessment for prediction of progression in glaucoma. , 2008, Investigative ophthalmology & visual science.
[48] F. Medeiros,et al. Frequency doubling technology perimetry abnormalities as predictors of glaucomatous visual field loss. , 2004, American journal of ophthalmology.
[49] J. Hanley,et al. The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.