Linda M. Zangwill

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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(More)
PURPOSE To determine whether neural network techniques can improve differentiation between glaucomatous and nonglaucomatous eyes, using the optic disc topography parameters of the Heidelberg Retina Tomograph (HRT; Heidelberg Engineering, Heidelberg, Germany). METHODS With the HRT, one eye was imaged from each of 108 patients with glaucoma (defined as(More)
PURPOSE To determine whether topographical measurements of the parapapillary region analyzed by machine learning classifiers can detect early to moderate glaucoma better than similarly processed measurements obtained within the disc margin and to improve methods for optimization of machine learning classifier feature selection. METHODS One eye of each of(More)
Assessment of optic disk size is an important, but often overlooked, component of the diagnostic evaluation for glaucoma. Measured values of optic disk size vary with the measurement technique utilized. Available methods for disk size measurement and their respective strengths and limitations will be discussed. Further, actual disk size varies with race and(More)
PURPOSE To determine if the patterns uncovered with variational Bayesian-independent component analysis-mixture model (VIM) applied to a large set of normal and glaucomatous fields obtained with the Swedish Interactive Thresholding Algorithm (SITA) are distinct, recognizable, and useful for modeling the severity of the field loss. METHODS SITA fields were(More)
Primary open angle glaucoma (POAG) is a progressive optic neuropathy characterized by retinal ganglion cell loss. Experimental primate glaucoma indicates neuronal degeneration of the lateral geniculate nucleus (LGN) and activity changes in the visual cortex (V1). Neuronal degeneration has also been shown in a post-mortem human study of the optic nerve, LGN(More)
PURPOSE To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). METHODS Seventy-two eyes of 72 healthy control subjects (average age = 64.3 +/- 8.8 years, visual field(More)
PURPOSE To evaluate the relationship between change in estimated retinal ganglion cell (RGC) counts and change in measures of functional and structural damage in glaucoma, from cross-sectional data. METHODS The study included 397 eyes of 397 patients with glaucoma, suspects, and healthy individuals. All eyes underwent testing with standard automated(More)
PURPOSE To estimate retinal ganglion cell (RGC) losses associated with the earliest development of visual field defects in glaucoma. DESIGN Observational cohort study. PARTICIPANTS The study group included 53 eyes of 53 patients with suspected glaucoma who were followed as part of the Diagnostic Innovations in Glaucoma (DIGS) study. These eyes had(More)
PURPOSE To present and evaluate a new method of estimating rates of retinal ganglion cell (RGC) loss in glaucoma by combining structural and functional measurements. DESIGN Observational cohort study. METHODS The study included 213 eyes of 213 glaucoma patients followed up for an average of 4.5 ± 0.8 years with standard automated perimetry visual fields(More)