Literature DB >> 26518076

How to detect progression in glaucoma.

Jayme R Vianna1, Balwantray C Chauhan2.   

Abstract

Detecting glaucoma progression remains one of the most challenging aspects of glaucoma management, since it can be hard to distinguish disease progression from exam variability and changes due to aging. In this review article, we discuss the use of perimetry, confocal scanning laser tomography and optical coherence tomography to detect glaucoma progression, and the techniques available to evaluate change with these modalities. Currently, there is no consensus on the best technique or criteria to detect glaucoma progression, or what amount of change would be clinically meaningful. New techniques have been developed to assess glaucoma progression, which make more comprehensive and complex use of data. They have the potential of detecting progression with better accuracy, with shorter follow-up periods, and generating better prognostics. Further validation of these new techniques is still required, but their incorporation into clinical practice is likely to yield significant benefits.
© 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Aging; Confocal scanning laser tomography; Glaucoma; Optic nerve head; Optical coherence tomography; Perimetry; Progression; Rate of change; Retinal nerve fiber layer

Mesh:

Year:  2015        PMID: 26518076     DOI: 10.1016/bs.pbr.2015.04.011

Source DB:  PubMed          Journal:  Prog Brain Res        ISSN: 0079-6123            Impact factor:   2.453


  16 in total

1.  A spatially varying change points model for monitoring glaucoma progression using visual field data.

Authors:  Samuel I Berchuck; Jean-Claude Mwanza; Joshua L Warren
Journal:  Spat Stat       Date:  2019-02-22

2.  Measuring Rates of Visual Field Progression in Linear Versus Nonlinear Scales: Implications for Understanding the Relationship Between Baseline Damage and Target Rates of Glaucoma Progression.

Authors:  Kevin Liebmann; Carlos Gustavo De Moraes; Jeffrey M Liebmann
Journal:  J Glaucoma       Date:  2017-08       Impact factor: 2.503

3.  Risk factors for visual field progression in newly diagnosed exfoliation glaucoma patients in Sweden.

Authors:  Marcelo Ayala
Journal:  Sci Rep       Date:  2022-06-24       Impact factor: 4.996

4.  Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method.

Authors:  Samuel I Berchuck; Jean-Claude Mwanza; Joshua L Warren
Journal:  J Am Stat Assoc       Date:  2019-04-01       Impact factor: 5.033

5.  Detecting Glaucomatous Progression With a Region-of-Interest Approach on Optical Coherence Tomography: A Signal-to-Noise Evaluation.

Authors:  Zhichao Wu; Abinaya Thenappan; Denis S D Weng; Robert Ritch; Donald C Hood
Journal:  Transl Vis Sci Technol       Date:  2018-02-28       Impact factor: 3.283

6.  Comparison of Widefield and Circumpapillary Circle Scans for Detecting Glaucomatous Neuroretinal Thinning on Optical Coherence Tomography.

Authors:  Zhichao Wu; Denis S D Weng; Abinaya Thenappan; Rashmi Rajshekhar; Robert Ritch; Donald C Hood
Journal:  Transl Vis Sci Technol       Date:  2018-06-04       Impact factor: 3.283

Review 7.  Medical Management of Glaucoma in the 21st Century from a Canadian Perspective.

Authors:  Paul Harasymowycz; Catherine Birt; Patrick Gooi; Lisa Heckler; Cindy Hutnik; Delan Jinapriya; Lesya Shuba; David Yan; Radmila Day
Journal:  J Ophthalmol       Date:  2016-11-08       Impact factor: 1.909

8.  Evaluation of a Qualitative Approach for Detecting Glaucomatous Progression Using Wide-Field Optical Coherence Tomography Scans.

Authors:  Zhichao Wu; Denis S D Weng; Rashmi Rajshekhar; Abinaya Thenappan; Robert Ritch; Donald C Hood
Journal:  Transl Vis Sci Technol       Date:  2018-05-01       Impact factor: 3.283

9.  A framework for assessing glaucoma progression using structural and functional indices jointly.

Authors:  Sampson Listowell Abu; Iván Marín-Franch; Lyne Racette
Journal:  PLoS One       Date:  2020-07-01       Impact factor: 3.240

10.  Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest.

Authors:  Christopher Bowd; Akram Belghith; Mark Christopher; Michael H Goldbaum; Massimo A Fazio; Christopher A Girkin; Jeffrey M Liebmann; Carlos Gustavo de Moraes; Robert N Weinreb; Linda M Zangwill
Journal:  Transl Vis Sci Technol       Date:  2021-07-01       Impact factor: 3.048

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