July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Gaussian Mixture Model Expectation Maximization for Glaucoma Progression Detection: An open-source R Package
Author Affiliations & Notes
  • Edward De Guzman
    Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Christopher Bowd
    University of California San Diego, California, California, United States
  • Michael Henry Goldbaum
    University of California San Diego, California, California, United States
  • Golnoush Sadat Mahmoudi Nezhad
    Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Siamak Yousefi
    Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States
    Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Footnotes
    Commercial Relationships   Edward De Guzman, None; Christopher Bowd, None; Michael Goldbaum, None; Golnoush Mahmoudi Nezhad, None; Siamak Yousefi, None
  • Footnotes
    Support  NEI R01EY022039 and R21EY027945
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 5116. doi:
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    • Get Citation

      Edward De Guzman, Christopher Bowd, Michael Henry Goldbaum, Golnoush Sadat Mahmoudi Nezhad, Siamak Yousefi; Gaussian Mixture Model Expectation Maximization for Glaucoma Progression Detection: An open-source R Package. Invest. Ophthalmol. Vis. Sci. 2018;59(9):5116.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To develop and assess the performance of an open-source R package for glaucomatous visual field change detection and monitoring progression (VFPro)

Methods : VFPro was trained on archival cross-sectional visual fields to identify 20 prevalent patterns of visual field defects in glaucoma using Gaussian Mixture Model Expectation Maximization and Principal Component Analysis. VFPro detects progression along these glaucomatous patterns of visual field defect. VFPro parameters were derived from a test-retest dataset including 133 eyes tested 10 times over 10 consecutive weeks to mimic no glaucomatous change. VFPro was then integrated into an R package (R programming language) and was made publicly available on the web. VFPro R package was applied on 270 VF sequences from 270 glaucomatous eyes from Rotterdam Eye Hospital in Netherland and was compared against global and pointwise analyses (parameters derived from the same test-retest dataset at 95% specificity). Survival analysis was used to compare all methods.

Results : At equal 95% specificity, VFPro identified more eyes as progressed followed by pointwise and global analyses. VFPro detected eyes earlier than pointwise and global, particularly towards the end of the survival curve, indicating identification of more slowly progressing eyes earlier than pointwise and global analyses.

Conclusions : VFPro software could be downloaded and used freely for glaucoma progression detection in glaucoma clinical care and research.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

Figure 1. Time to detect progression using global, pointwise, and VFPro analyses in a dataset including 270 glaucoma eyes. VFPro detected more eyes as progressed, particularly, more slowly progressing eyes than other methods.

Figure 1. Time to detect progression using global, pointwise, and VFPro analyses in a dataset including 270 glaucoma eyes. VFPro detected more eyes as progressed, particularly, more slowly progressing eyes than other methods.

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