July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
The Relationship Between Machine Learning-Derived Binocular Patterns of Visual Field Loss and Patient-Reported Disability in Glaucoma
Author Affiliations & Notes
  • Nara Ogata
    Duke University, Durham, North Carolina, United States
  • Alessandro A Jammal
    Duke University, Durham, North Carolina, United States
  • Eduardo Bicalho Mariottoni
    Duke University, Durham, North Carolina, United States
  • Carla Urata
    Duke University, Durham, North Carolina, United States
  • Susan Wakil
    Duke University, Durham, North Carolina, United States
  • Felipe A Medeiros
    Duke University, Durham, North Carolina, United States
  • Footnotes
    Commercial Relationships   Nara Ogata, None; Alessandro Jammal, None; Eduardo Mariottoni, None; Carla Urata, None; Susan Wakil, None; Felipe Medeiros, Allergan (C), Allergan (F), Bausch&Lomb (F), Carl Zeiss Meditec (C), Carl Zeiss Meditec (F), Heidelberg Engineering (F), Merck (F), nGoggle Inc. (F), Novartis (C), Reichert (C), Reichert (R), Sensimed (C), Topcon (C)
  • Footnotes
    Support  EY027651, EY025056, EY021818
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2440. doi:https://doi.org/
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      Nara Ogata, Alessandro A Jammal, Eduardo Bicalho Mariottoni, Carla Urata, Susan Wakil, Felipe A Medeiros; The Relationship Between Machine Learning-Derived Binocular Patterns of Visual Field Loss and Patient-Reported Disability in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2440. doi: https://doi.org/.

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

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Abstract

Purpose : To investigate whether specific binocular patterns of visual field loss, derived from unsupervised machine learning classification, are related to patient-reported disability in glaucoma.

Methods : The study included 677 patients with glaucomatous visual field damage in at least one eye. Patient-reported vision-related quality of life was assessed by the National Eye Institute Questionnaire (NEI VFQ-25) questionnaire. A latent class analysis procedure was used to divide subjects into two classes (disabled versus non-disabled) based on NEI VFQ-25 results. Total deviation values for the right and left eyes were extracted from the 52 locations measured by standard automated perimetry (SAP), and the binocular summation was done following a previously described method. An unsupervised machine learning method was used to classify binocular visual field abnormalities into different patterns.

Results : One hundred eleven of the 677 (16.4%) subjects were classified as having vision-related disability based on NEI VFQ-25 results. Among the 5 patterns of binocular visual field loss, 4 of them showed significant association with disability (P<0.001, P=0.008, P=0.004, and P=0.009, respectively).

Conclusions : Machine learning-derived binocular patterns of visual field loss are related to patient-reported vision-related quality of life in glaucoma.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

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