Literature DB >> 27802515

Glaucoma-Diagnostic Ability of Ganglion Cell-Inner Plexiform Layer Thickness Difference Across Temporal Raphe in Highly Myopic Eyes.

Young Kook Kim1, Byeong Wook Yoo2, Jin Wook Jeoung1, Hee Chan Kim3, Hae Jin Kim1, Ki Ho Park1.   

Abstract

PURPOSE: To evaluate the glaucoma-diagnostic ability of the ganglion cell-inner plexiform layer (GCIPL) thickness difference across the temporal raphe in highly myopic eyes.
METHODS: We consecutively enrolled a total of 195 highly myopic eyes (axial length [AL] >26.5 mm) of 195 subjects: 93 glaucoma patients along with and 102 nonglaucomatous subjects. Cirrus high-definition optical coherence tomography (OCT) was employed to scan all of the subjects' macular and optic discs. Using a MATLAB-based customized program (the GCIPL hemifield test), a positive test result was automatically declared if the following two conditions were met: (1) the horizontal line is detected for longer than one-half of the distance from the temporal inner elliptical annulus to the outer elliptical annulus, and (2) the average GCIPL thickness difference within 10 pixels of the reference line, both above and below, is 5 μm or more. The glaucoma-diagnostic ability was computed using the area under the receiver operating characteristic curve (AUC).
RESULTS: Among the glaucomatous eyes, GCIPL hemifield test positivity was shown in 92.5% (86 of 93), significantly higher than that for the nonglaucomatous eyes (4.90%, 5 of 102; P <0.001). The value of AUC for the GCIPL hemifield test was excellent (0.938; sensitivity 92.50%, specificity 95.10%) and was the best compared with those for any of OCT parameters.
CONCLUSIONS: In highly myopic eyes, determination of the presence or absence of GCIPL thickness difference across the temporal raphe via OCT macula scan can be a useful means of distinguishing the glaucomatous damage.

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Year:  2016        PMID: 27802515     DOI: 10.1167/iovs.16-20116

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


  9 in total

1.  Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients.

Authors:  Jinho Lee; Young Kook Kim; Jin Wook Jeoung; Ahnul Ha; Yong Woo Kim; Ki Ho Park
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2.  OCTA vessel density changes in the macular zone in glaucomatous eyes.

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Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2018-04-10       Impact factor: 3.117

3.  Optical Coherence Tomography Angiography Macular Vascular Density Measurements and the Central 10-2 Visual Field in Glaucoma.

Authors:  Rafaella C Penteado; Linda M Zangwill; Fábio B Daga; Luke J Saunders; Patricia I C Manalastas; Takuhei Shoji; Tadamichi Akagi; Mark Christopher; Adeleh Yarmohammadi; Sasan Moghimi; Robert N Weinreb
Journal:  J Glaucoma       Date:  2018-06       Impact factor: 2.503

4.  Macular Optical Coherence Tomography Imaging in Glaucoma.

Authors:  Alireza Kamalipour; Sasan Moghimi
Journal:  J Ophthalmic Vis Res       Date:  2021-07-29

5.  The Fovea-BMO Axis Angle and Macular Thickness Vertical Asymmetry Across The Temporal Raphe.

Authors:  Zeinab Ghassabi; Andrew H Nguyen; Navid Amini; Sharon Henry; Joseph Caprioli; Kouros Nouri-Mahdavi
Journal:  J Glaucoma       Date:  2018-11       Impact factor: 2.503

6.  Optical coherence tomography for glaucoma diagnosis: An evidence based meta-analysis.

Authors:  Vinay Kansal; James J Armstrong; Robert Pintwala; Cindy Hutnik
Journal:  PLoS One       Date:  2018-01-04       Impact factor: 3.240

7.  Combined wide-field optical coherence tomography angiography density map for high myopic glaucoma detection.

Authors:  Yu Jeong Kim; Kyeong Ik Na; Han Woong Lim; Mincheol Seong; Won June Lee
Journal:  Sci Rep       Date:  2021-11-11       Impact factor: 4.379

8.  Difference in patterns of retinal ganglion cell damage between primary open-angle glaucoma and non-arteritic anterior ischaemic optic neuropathy.

Authors:  Yeon Hee Lee; Kyoung Nam Kim; Dong Won Heo; Tae Seen Kang; Sung Bok Lee; Chang-Sik Kim
Journal:  PLoS One       Date:  2017-10-26       Impact factor: 3.240

9.  Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model.

Authors:  Jinho Lee; Young Kook Kim; Ahnul Ha; Sukkyu Sun; Yong Woo Kim; Jin-Soo Kim; Jin Wook Jeoung; Ki Ho Park
Journal:  Sci Rep       Date:  2020-02-24       Impact factor: 4.379

  9 in total

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