PURPOSE: To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma. METHODS: Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained and tested by cross validation on the numerical plot of absolute sensitivity plus age of 189 normal eyes and 156 glaucomatous eyes, designated as such by the appearance of the optic nerve. The authors compared performance of these classifiers with the global indices of STATPAC, using the area under the ROC curve. Two human experts were judged against the machine classifiers and the global indices by plotting their sensitivity-specificity pairs. RESULTS: MoG had the greatest area under the ROC curve of the machine classifiers. Pattern SD (PSD) and corrected PSD (CPSD) had the largest areas under the curve of the global indices. MoG had significantly greater ROC area than PSD and CPSD. Human experts were not better at classifying visual fields than the machine classifiers or the global indices. CONCLUSIONS: MoG, using the entire visual field and age for input, interpreted SAP better than the global indices of STATPAC. Machine classifiers may augment the global indices of STATPAC.
PURPOSE: To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma. METHODS: Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained and tested by cross validation on the numerical plot of absolute sensitivity plus age of 189 normal eyes and 156 glaucomatous eyes, designated as such by the appearance of the optic nerve. The authors compared performance of these classifiers with the global indices of STATPAC, using the area under the ROC curve. Two human experts were judged against the machine classifiers and the global indices by plotting their sensitivity-specificity pairs. RESULTS: MoG had the greatest area under the ROC curve of the machine classifiers. Pattern SD (PSD) and corrected PSD (CPSD) had the largest areas under the curve of the global indices. MoG had significantly greater ROC area than PSD and CPSD. Human experts were not better at classifying visual fields than the machine classifiers or the global indices. CONCLUSIONS: MoG, using the entire visual field and age for input, interpreted SAP better than the global indices of STATPAC. Machine classifiers may augment the global indices of STATPAC.
Authors: Michael H Goldbaum; Pamela A Sample; Zuohua Zhang; Kwokleung Chan; Jiucang Hao; Te-Won Lee; Catherine Boden; Christopher Bowd; Rupert Bourne; Linda Zangwill; Terrence Sejnowski; David Spinak; Robert N Weinreb Journal: Invest Ophthalmol Vis Sci Date: 2005-10 Impact factor: 4.799
Authors: Christopher Bowd; Intae Lee; Michael H Goldbaum; Madhusudhanan Balasubramanian; Felipe A Medeiros; Linda M Zangwill; Christopher A Girkin; Jeffrey M Liebmann; Robert N Weinreb Journal: Invest Ophthalmol Vis Sci Date: 2012-04-30 Impact factor: 4.799
Authors: Osamah J Saeedi; Tobias Elze; Loris D'Acunto; Ramya Swamy; Vikram Hegde; Surabhi Gupta; Amin Venjara; Joby Tsai; Jonathan S Myers; Sarah R Wellik; Carlos Gustavo De Moraes; Louis R Pasquale; Lucy Q Shen; Michael V Boland Journal: Ophthalmology Date: 2019-02-04 Impact factor: 12.079
Authors: Michael H Goldbaum; Irina Falkenstein; Igor Kozak; Jiucang Hao; Dirk-Uwe Bartsch; Terrance Sejnowski; William R Freeman Journal: Trans Am Ophthalmol Soc Date: 2008