June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Artificial Intelligence Characterization of Spatial Patterns of Microvascular Dropout in Glaucoma
Author Affiliations & Notes
  • Alireza Kamalipour
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Sasan Moghimi
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Mohammad Sadegh Jazayeri
    San Diego State University, San Diego, California, United States
  • Pooya Khosravi
    University of California Irvine, Irvine, California, United States
  • James A Proudfoot
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Huiyuan Hou
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Takashi Nishida
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Adeleh Yarmohammadi
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Ryan Caezar Calaguas David
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Nevin W. El-Nimri
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Jasmin Rezapour
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Christopher Bowd
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Linda M Zangwill
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Robert N Weinreb
    Ophthalmology, University of California at San Diego Department of Ophthalmology at the Shiley Eye Institute, La Jolla, California, United States
  • Footnotes
    Commercial Relationships   Alireza Kamalipour, None; Sasan Moghimi, None; Mohammad Sadegh Jazayeri, None; Pooya Khosravi, None; James Proudfoot, None; Huiyuan Hou, None; Takashi Nishida, None; Adeleh Yarmohammadi, None; Ryan Caezar David, None; Nevin El-Nimri, None; Jasmin Rezapour, None; Christopher Bowd, None; Linda Zangwill, Carl Zeiss Meditec Inc. (F), Heidelberg Engineering GmbH (F), Optovue Inc. (F), Topcon Medical Systems Inc. (F), Zeiss Meditec (P); Robert Weinreb, Allergan (C), Bausch&Lomb (C), Bausch&Lomb (F), Carl Zeiss Meditec (F), Centervue (F), Equinox (C), Eyenovia (C), Heidelberg Engineering (F), Konan Medical (F), Meditec-Zeiss (P), Optovue (F), Toromedes (P)
  • Footnotes
    Support  National Eye Institute R01EY029058, R01EY11008, R01EY19869, R01EY027510, EY026574, P30EY022589, Tobacco-Related Disease Research Program T31IP1511, and an unrestricted grant from Research to Prevent Blindness (New York,NY)
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 1768. doi:
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    • Get Citation

      Alireza Kamalipour, Sasan Moghimi, Mohammad Sadegh Jazayeri, Pooya Khosravi, James A Proudfoot, Huiyuan Hou, Takashi Nishida, Adeleh Yarmohammadi, Ryan Caezar Calaguas David, Nevin W. El-Nimri, Jasmin Rezapour, Christopher Bowd, Linda M Zangwill, Robert N Weinreb; Artificial Intelligence Characterization of Spatial Patterns of Microvascular Dropout in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2021;62(8):1768.

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

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Abstract

Purpose : To characterize the spatial patterns of microvascular dropout in glaucoma using an unsupervised artificial intelligence approach.

Methods : This retrospective cohort study included a total of 1899 Angiovue optic nerve head optical coherence tomography angiography (OCTA) scans of 707 eyes of 397 healthy, glaucoma suspect and glaucoma patients from the Diagnostic Innovations in Glaucoma Study. An unsupervised artificial intelligence technique “Archetypal Analysis” was applied to pre-processed en-face OCTA images obtained at the level of the retinal peripapillary capillary density network. Correlations of OCTA derived spatial patterns with 24-2 visual field (VF) mean deviation (MD), 24-2 VF pattern standard deviation (PSD), 10-2 VF-MD, and 10-2 VF-PSD were evaluated. Diagnostic accuracy of spatial patterns to detect past glaucoma progression was calculated and compared with those of circumpapillary capillary density (cpCD) and retinal nerve fiber layer thickness (cpRNFLT) using 310 eyes (of 195 glaucoma suspect and glaucoma patients) with a minimum of 3 years of follow-up and 5 reliable 24-2 VF tests before their last OCTA image. Progression was defined based on an event-based glaucoma progression analysis (GPA) criterion. Generalized mixed-effects model was used to adjust for correlations of different metrics at the patient and eye levels.

Results : Eleven distinct spatial patterns were identified across the spectrum of disease severity (Figure 1). Notably, 10 of the 11 patterns of microvascular loss preserved the less vulnerable papillomacular area. Eight (1,5,6,7,8,9,10,11), 5 (1,7,8,10,11), 5 (1,8,9,10,11) and 5 (1,3,8,10,11) spatial patterns were significantly associated with 24-2 MD, 24-2 PSD, 10-2 MD and 10-2 PSD, respectively (P<0.05 for all). The archetypal model (area under the receiver operating characteristic curve [AUC]=0.75) outperformed cpCD (AUC=0.66) and cpRNFLT (AUC=0.65) in detecting past glaucoma progression (P=0.017).

Conclusions : Unsupervised artificial intelligence techniques are capable of identifying patterns of microvascular dropout in glaucoma using OCTA images. These patterns show promise not only in qualitative evaluation but also in the quantitative assessment of glaucomatous microvascular dropout associated with VF damage and VF progression.

This is a 2021 ARVO Annual Meeting abstract.

 

Figure 1: Spatial patterns (SP) of microvascular dropout (right eye format) in glaucoma identified by archetypal analysis.

Figure 1: Spatial patterns (SP) of microvascular dropout (right eye format) in glaucoma identified by archetypal analysis.

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