Cargando…

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields

PURPOSE: To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM–progression of patterns (POP) and VIM-POP). To compare GEM-...

Descripción completa

Detalles Bibliográficos
Autores principales:	Yousefi, Siamak, Balasubramanian, Madhusudhanan, Goldbaum, Michael H., Medeiros, Felipe A., Zangwill, Linda M., Weinreb, Robert N., Liebmann, Jeffrey M., Girkin, Christopher A., Bowd, Christopher
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	The Association for Research in Vision and Ophthalmology 2016
Materias:	Articles
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4855479/ https://www.ncbi.nlm.nih.gov/pubmed/27152250 http://dx.doi.org/10.1167/tvst.5.3.2

Ejemplares similares

Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers
por: Bowd, Christopher, et al.
Publicado: (2014)

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
por: Christopher, Mark, et al.
Publicado: (2018)

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression
por: Christopher, Mark, et al.
Publicado: (2018)

Structural Change Can Be Detected in Advanced-Glaucoma Eyes
por: Belghith, Akram, et al.
Publicado: (2016)

Heidelberg Edge Perimetry for the Detection of Early Glaucomatous Damage: A Case Report
por: Marvasti, Amir H., et al.
Publicado: (2013)

Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
por: Bowd, Christopher, et al.
Publicado: (2021)

Macular Ganglion Cell Inner Plexiform Layer Thickness in Glaucomatous Eyes with Localized Retinal Nerve Fiber Layer Defects
por: Zhang, Chunwei, et al.
Publicado: (2016)

Racial Differences in the Association of Anterior Lamina Cribrosa Surface Depth and Glaucoma Severity in the African Descent and Glaucoma Evaluation Study (ADAGES)
por: Girkin, Christopher A., et al.
Publicado: (2019)

Racial Differences in the Rate of Change in Anterior Lamina Cribrosa Surface Depth in the African Descent and Glaucoma Evaluation Study
por: Girkin, Christopher A., et al.
Publicado: (2021)

Central contrast sensitivity perimetry discriminates between glaucomatous and non-glaucomatous eyes
por: Abu, Emmanuel Kwasi, et al.
Publicado: (2023)

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization
por: Fan, Rui, et al.
Publicado: (2022)

Progression of Early Glaucomatous Damage: Performance of Summary Statistics From Optical Coherence Tomography and Perimetry
por: Tsamis, Emmanouil, et al.
Publicado: (2023)

Full-field perimetry for evaluation of glaucomatous (presumed) cup
por: Kummararaj, Govindarajan, et al.
Publicado: (2012)

Contrast Sensitivity Perimetry and Clinical Measures of Glaucomatous Damage
por: Swanson, William H., et al.
Publicado: (2014)

Challenges to detect glaucomatous visual field loss with pupil perimetry
por: Asakawa, Ken, et al.
Publicado: (2019)

The Relationship between Visual Field Index and Estimated Number of Retinal Ganglion Cells in Glaucoma
por: Marvasti, Amir H., et al.
Publicado: (2013)

Assessment of Choroidal Thickness in Healthy and Glaucomatous Eyes Using Swept Source Optical Coherence Tomography
por: Zhang, Chunwei, et al.
Publicado: (2014)

Challenges to detect glaucomatous visual field loss with pupil perimetry [Corrigendum]
Publicado: (2019)

Estimated Rates of Retinal Ganglion Cell Loss in Glaucomatous Eyes with and without Optic Disc Hemorrhages
por: Gracitelli, Carolina P. B., et al.
Publicado: (2014)

Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding
por: Uğur, Yiğit, et al.
Publicado: (2020)

Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study
por: Zhalechian, Mohammad, et al.
Publicado: (2021)

Does the Location of Bruch's Membrane Opening Change Over Time? Longitudinal Analysis Using San Diego Automated Layer Segmentation Algorithm (SALSA)
por: Belghith, Akram, et al.
Publicado: (2016)

Unsupervised assessment of microarray data quality using a Gaussian mixture model
por: Howard, Brian E, et al.
Publicado: (2009)

GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture Model
por: Zhu, Weijin, et al.
Publicado: (2022)

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms
por: Christopher, Mark, et al.
Publicado: (2020)

A Batch Rival Penalized Expectation-Maximization Algorithm for Gaussian Mixture Clustering with Automatic Model Selection
por: Wen, Jiechang, et al.
Publicado: (2012)

Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization
por: Qiao, Ju, et al.
Publicado: (2019)

Detection of central visual field defects in early glaucomatous eyes: Comparison of Humphrey and Octopus perimetry
por: Roberti, Gloria, et al.
Publicado: (2017)

Detection of Early Glaucomatous Damage: Performance of Summary Statistics From Optical Coherence Tomography and Perimetry
por: Tsamis, Emmanouil, et al.
Publicado: (2022)

Research on a Segmentation Algorithm for the Tujia Brocade Images Based on Unsupervised Gaussian Mixture Clustering
por: He, Shuqi
Publicado: (2021)

Development of a test grid using Eye Movement Perimetry for screening glaucomatous visual field defects
por: Kadavath Meethal, N. S., et al.
Publicado: (2017)

The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy
por: Wu, Weisan
Publicado: (2021)

Goldmann V Standard Automated Perimetry Underestimates Central Visual Sensitivity in Glaucomatous Eyes with Increased Axial Length
por: Yanagisawa, Mieko, et al.
Publicado: (2017)

The Effective Dynamic Ranges for Glaucomatous Visual Field Progression With Standard Automated Perimetry and Stimulus Sizes III and V
por: Wall, Michael, et al.
Publicado: (2018)

Investigation of associations between Piezo1 mechanoreceptor gain-of-function variants and glaucoma-related phenotypes in humans and mice
por: Baxter, Sally L., et al.
Publicado: (2020)

Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
por: Zhong, Jun, et al.
Publicado: (2021)

The role of hemifield sector analysis in multifocal visual evoked potential objective perimetry in the early detection of glaucomatous visual field defects
por: Mousa, Mohammad F, et al.
Publicado: (2013)

Reversal of a glaucomatous optic disc pit
por: Kim, Bum Gi, et al.
Publicado: (2021)

Evaluation of Hemifield Sector Analysis Protocol in Multifocal Visual Evoked Potential Objective Perimetry for the Diagnosis and Early Detection of Glaucomatous Field Defects
por: Mousa, Mohammad F., et al.
Publicado: (2014)

Expectation-Maximization Algorithm for the Calibration of Complex Simulator Using a Gaussian Process Emulator
por: Seo, Yun Am, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_cqkbklr9iivu4gvpol2urnh0v6