Literature DB >> 24744205

Diffuse retinal nerve fiber layer defects identification and quantification in thickness maps.

Joong Won Shin1, Ki Bang Uhm1, Mincheol Seong1, Yu Jeong Kim1.   

Abstract

PURPOSE: To report retinal nerve fiber layer (RNFL) defect identification and quantification in RNFL thickness maps according to the structural RNFL loss, and to evaluate diffuse RNFL defects.
METHODS: A total of 170 patients with glaucoma and 186 normal subjects were consecutively enrolled. We defined RNFL defects in an RNFL thickness map by the degree of RNFL loss. The reference level for RNFL defect determination was set as a 20% to 70% degree of RNFL loss with a 1% interval. To identify RNFL defects, each individual RNFL thickness map was compared to the normative database map by using MATLAB software, and the region below the reference level was detected. The area, volume, location, and angular width of each RNFL defect were measured. Diffuse RNFL defects were defined as having an angular width > 30°.
RESULTS: The optimal reference level for glaucomatous RNFL defects identification was 42% loss of RNFL. Retinal nerve fiber layer defects were identified in all (100%) of the 170 glaucoma patients and false-positive RNFL defects were detected in 16 (8.16%) cases among the 186 normal subjects. In all, 64.1% of glaucoma patients had diffuse RNFL defects, and 47.7% of diffuse RNFL defects were associated with mild glaucoma patients. The volume of diffuse RNFL defects was significantly associated with the severity of glaucomatous damage (P = 0.009). Diffuse RNFL defects were located closer to the center of the optic disc than localized RNFL defects (P < 0.001).
CONCLUSIONS: Retinal nerve fiber layer thickness map analysis is an effective method for analyzing RNFL defects. Quantitative measurements (area, volume, location, and width) were useful to understanding diffuse RNFL defects. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Entities:  

Keywords:  OCT; RNFL thickness map; diffuse RNFL defect; retinal nerve fiber layer

Mesh:

Year:  2014        PMID: 24744205     DOI: 10.1167/iovs.13-13181

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  9 in total

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Journal:  Am J Ophthalmol       Date:  2017-08-12       Impact factor: 5.258

2.  Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression.

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3.  Glaucoma progression detection using nonlocal Markov random field prior.

Authors:  Akram Belghith; Christopher Bowd; Felipe A Medeiros; Madhusudhanan Balasubramanian; Robert N Weinreb; Linda M Zangwill
Journal:  J Med Imaging (Bellingham)       Date:  2014-12-29

4.  The Effect of Optic Disc Center Displacement on Retinal Nerve Fiber Layer Measurement Determined by Spectral Domain Optical Coherence Tomography.

Authors:  Joong Won Shin; Yong Un Shin; Ki Bang Uhm; Kyung Rim Sung; Min Ho Kang; Hee Yoon Cho; Mincheol Seong
Journal:  PLoS One       Date:  2016-10-26       Impact factor: 3.240

5.  Diagnostic Ability of Retinal Nerve Fiber Layer Thickness Deviation Map for Localized and Diffuse Retinal Nerve Fiber Layer Defects.

Authors:  Joong Won Shin; Mincheol Seong; Jung Wook Lee; Eun Hee Hong; Ki Bang Uhm
Journal:  J Ophthalmol       Date:  2017-01-10       Impact factor: 1.909

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Authors:  Stephanie Choi; Firas Jassim; Edem Tsikata; Ziad Khoueir; Linda Y Poon; Boy Braaf; Benjamin J Vakoc; Brett E Bouma; Johannes F de Boer; Teresa C Chen
Journal:  Transl Vis Sci Technol       Date:  2020-02-12       Impact factor: 3.283

7.  Three-Dimensional Optical Coherence Tomography Imaging For Glaucoma Associated With Boston Keratoprosthesis Type I and II.

Authors:  Ziad Khoueir; Firas Jassim; Boy Braaf; Linda Yi-Chieh Poon; Edem Tsikata; James Chodosh; Claes H Dohlman; Benjamin J Vakoc; Brett E Bouma; Johannes F de Boer; Teresa C Chen
Journal:  J Glaucoma       Date:  2019-08       Impact factor: 2.503

8.  Deep learning on fundus images detects glaucoma beyond the optic disc.

Authors:  Ruben Hemelings; Bart Elen; João Barbosa-Breda; Matthew B Blaschko; Patrick De Boever; Ingeborg Stalmans
Journal:  Sci Rep       Date:  2021-10-13       Impact factor: 4.379

9.  Widefield OCT Imaging for Quantifying Inner Retinal Thickness in the Nonhuman Primate.

Authors:  Varsha Venkata Srinivasan; Siddarth Das; Nimesh Patel
Journal:  Transl Vis Sci Technol       Date:  2022-08-01       Impact factor: 3.048

  9 in total

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