July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Defect Classes of Visual Field Measurement in Glaucoma
Author Affiliations & Notes
  • Jorryt Gerlof Tichelaar
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Mengyu Wang
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Lucy Q Shen
    Mass. Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Louis R Pasquale
    Icahn School of Medicine at Mount Sinai, New York Eye and Eye Infirmary of Mount Sinai, New York City, New York, United States
  • Michael V Boland
    Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
  • Sarah R Wellik
    Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, United States
  • Carlos Gustavo de Moraes
    Edward S. Harkness Eye Institute, Columbia University Medical Center, New York City, New York, United States
  • Jonathan S Myers
    Wills Eye Hospital, Philadelphia, Pennsylvania, United States
  • Peter Bex
    Department of Psychology, Northeastern University, Boston, Massachusetts, United States
  • Osamah Saeedi
    Department of Ophthalmology and Visual Sciences, University of Maryland Medical Center, College Park, Maryland, United States
  • Neda Baniasadi
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Dian Li
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Hui Wang
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
    Institute for Psychology and Behavior, Jilin University of Finance and Economics, Changchun, Jilin, China
  • Tobias Elze
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Jorryt Tichelaar, None; Mengyu Wang, Adaptive Sensory Technology (R); Lucy Shen, Topcon - Research funding (C), Topcon - Research funding (F); Louis Pasquale, Alcon-Speaker (S), Bausch+Lomb (C), Eyenovia-Advisory board member (S), Verily Life Science (F); Michael Boland, Heidelberg (C); Sarah Wellik, None; Carlos de Moraes, Carl Zeiss Meditec, Inc. (F), Galimedix, Inc. (C), GmBH (F), Heidelberg Engineering (F), Novartis, Inc. (C), Topcon, Inc. (F); Jonathan Myers, None; Peter Bex, United States PCT/US2014/052414 (P); Osamah Saeedi, None; Neda Baniasadi, Adaptive Sensory Technology (R); Dian Li, Adaptive Sensory Technology (R); Hui Wang, None; Tobias Elze, Adaptive Sensory Technology (R), United States PCT/US2014/052414 (P)
  • Footnotes
    Support  BrightFocus Foundation, Research to Prevent Blindness, NEI Core Grant P30EYE003790, NEI RO1 015473, Lions Foundation, Grimshaw-Gudewicz Foundation, Alice Adler Fellowship
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2858. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Jorryt Gerlof Tichelaar, Mengyu Wang, Lucy Q Shen, Louis R Pasquale, Michael V Boland, Sarah R Wellik, Carlos Gustavo de Moraes, Jonathan S Myers, Peter Bex, Osamah Saeedi, Neda Baniasadi, Dian Li, Hui Wang, Tobias Elze; Defect Classes of Visual Field Measurement in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2858.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Glaucomatous visual field (VF) loss is diagnosed and quantified by summary measures that do not provide information about specific glaucomatous patterns. Here, we computationally trace the evolution of VF loss patterns over time to develop a quantitative model of functional glaucoma subtypes (defect classes).

Methods : To determine representative patterns of vision loss, total and mean deviations (MD) of the most recent reliable VFs (SITA Standard 24-2) were retrospectively selected among all patients from five clinical glaucoma practices. VFs were grouped by severity into MD bins of 1dB width. Separately for each MD bin, archetypal analysis, an unsupervised machine learning technique, was applied to autonomously determine severity specific patterns (archetypes [ATs]). To trace their evolution over time, all eyes with multiple VFs were selected. Each VF was quantitatively decomposed into the ATs of each MD bin by weighting the AT coefficients according to the measurement uncertainty distribution of the respective measured MD (Fig. 1A). For each baseline-follow up VF pair, an evolution matrix of the respective AT coefficients is calculated (Fig. 1B). Pooling over all evolution matrices, for each AT within each MD bin, the most probable follow-up AT for each next more severe stage was calculated, which yielded pattern trajectories that define defect classes.

Results : Based on 179,936 VFs from 179,936 eyes, between 7 and 14 ATs were identified for each MD bin. Based on baseline follow-up evolution matrices from 278,445 VFs from 74,806 eyes, for moderate severity (-6dB≥MD>-12dB), seven distinct defect classes could be determined that contain features which are compatible with nerve fiber defects (Fig. 2A): A single upper peripheral class as well as upper and lower hemifield pairs of paracentral, nasal, and temporal patterns. Nasal, but not temporal to the blind spot, the classes respect the horizontal midline. When evolution patterns over the entire severity range are assessed, distinctive features like presence/absence of central VF loss are preserved within the respective defect classes (e.g. Fig. 2B).

Conclusions : We introduce VF defect classes which allow quantitative defect classifications of arbitrary VF measurements, which may help to detect glaucoma progression and enhance subtype-specific structure-function modeling.

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

 

 

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×