Literature DB >> 31481390

Predicting Humphrey 10-2 visual field from 24-2 visual field in eyes with advanced glaucoma.

Kenji Sugisaki1, Ryo Asaoka2, Toshihiro Inoue3, Keiji Yoshikawa4, Akiyasu Kanamori5, Yoshio Yamazaki6, Shinichiro Ishikawa7, Hodaka Nemoto8, Aiko Iwase9, Makoto Araie10.   

Abstract

AIMS: To predict Humphrey Field Analyzer Central 10-2 Swedish Interactive Threshold Algorithm-Standard test (HFA 10-2) results (Carl Zeiss Meditec, San Leandro, CA) from HFA 24-2 results of the same eyes with advanced glaucoma.
METHODS: Training and testing HFA 24-2 and 10-2 data sets, respectively, consisted of 175 eyes (175 patients) and 44 eyes (44 patients) with open advanced glaucoma (mean deviation of HFA 24-2 ≤-20 dB). Using the training data set, the 68 total deviation (TD) values of the HFA 10-2 test points were predicted from those of the innermost 16 HFA 24-2 test points in the same eye, using image processing or various machine learning methods including bilinear interpolation (IP) as a standard for comparison. The absolute prediction error (PredError) was calculated by applying each method to the testing data set.
RESULTS: The mean (SD) test-retest variability of the HFA 10-2 results in the testing data set was 2.1±1.0 dB, while the IP method yielded a PredError of 5.0±1.7 dB. Among the methods tested, support vector regression (SVR) provided a smallest PredError (4.0±1.5 dB). SVR predicted retinal sensitivity at HFA 10-2 test points in the preserved 'central isle' of advanced glaucoma from HFA 24-2 results of the same eye within an error range of about 25%, while error range was approximately twice of the test-retest variability.
CONCLUSION: Applying SVR to HFA 24-2 results allowed us to predict TD values at HFA 10-2 test points of the same eye with advanced glaucoma with an error range of about 25%. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  field of vision; glaucoma

Year:  2019        PMID: 31481390     DOI: 10.1136/bjophthalmol-2019-314170

Source DB:  PubMed          Journal:  Br J Ophthalmol        ISSN: 0007-1161            Impact factor:   4.638


  2 in total

1.  Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images.

Authors:  Shotaro Asano; Ryo Asaoka; Hiroshi Murata; Yohei Hashimoto; Atsuya Miki; Kazuhiko Mori; Yoko Ikeda; Takashi Kanamoto; Junkichi Yamagami; Kenji Inoue
Journal:  Sci Rep       Date:  2021-01-26       Impact factor: 4.379

2.  A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT.

Authors:  Ryo Asaoka; Linchuan Xu; Hiroshi Murata; Taichi Kiwaki; Masato Matsuura; Yuri Fujino; Masaki Tanito; Kazuhiko Mori; Yoko Ikeda; Takashi Kanamoto; Kenji Inoue; Jukichi Yamagami; Kenji Yamanishi
Journal:  Ophthalmol Sci       Date:  2021-09-07
  2 in total

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