June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Artificial Intelligence Assessment of Optic Cup Shape Patterns in Glaucoma
Author Affiliations & Notes
  • Mengyu Wang
    Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Lucy Q Shen
    Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Louis R Pasquale
    Eye and Vision Research Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Thao D. Nguyen
    Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
  • Yangjiani Li
    Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Mohammad Eslami
    Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Chhavi Saini
    Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Nazlee Zebardast
    Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Tobias Elze
    Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Mengyu Wang, None; Lucy Shen, Topcon (F); Louis Pasquale, Emerald Biosciences (C), Eyenovia (C), Twenty Twenty (C); Thao Nguyen, None; Yangjiani Li, None; Mohammad Eslami, None; Chhavi Saini, None; Nazlee Zebardast, None; Tobias Elze, None
  • Footnotes
    Support  This work was supported by NIH K99 EY028631 (M.W.), NIH R21 EY030142 (T.E.), NIH R21 EY030631 (T.E.), NIH R01 EY030575 (T.E.) and NIH R01 EY015473 (L.R.P.).
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 3359. doi:
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    • Get Citation

      Mengyu Wang, Lucy Q Shen, Louis R Pasquale, Thao D. Nguyen, Yangjiani Li, Mohammad Eslami, Chhavi Saini, Nazlee Zebardast, Tobias Elze; Artificial Intelligence Assessment of Optic Cup Shape Patterns in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2021;62(8):3359.

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

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Abstract

Purpose : To assess optic cup shape patterns in glaucoma with unsupervised artificial intelligence (AI).

Methods : The first Cirrus OCT scans of the optic nerve head (ONH) from each eye with signal strength ≥ 6 were selected. The optic cup shape was represented as the vertical positions of the inner limiting membrane (ILM) with respect to the lowest ILM vertical position in each eye. Scans with ONH centers deviating more than 0.3 mm from the scan center were excluded. The OCT scans were registered with respect to ONH centers in the right eye format. The Humphrey SITA standard 24-2 visual fields (VFs) tested within three months of the OCT tests were selected. An unsupervised AI method termed non-negative matrix factorization was applied to assess the cup shape patterns. The cup shape patterns were correlated with VF and OCT diagnostic parameters. We compared if using the cup shape patterns improved the prediction of VF loss using linear regression with model selection to remove redundant features.

Results : We determined 14 cup shape patterns (Figure 1) from 9,854 OCT scans. The brighter regions in the cup shape patterns indicate the more informative zones with greater variations across patients. Mean deviation (MD), retinal nerve fiber layer thickness (RNFLT) and ONH related parameters including rim area, disc area, average cup-disc (CD) ratio, vertical CD ratio and cup volume (Table 1) were most negatively correlated with Patterns 5, 4, 4, 12, 14, 14 and 14 (r: -0.15, -0.21, -0.45, -0.42, -0.60, -0.57 and -0.49, p < 0.001), and were most positively correlated with Patterns 10, 10, 14, 5, 5, 7 and 5 (r: 0.30, 0.28, 0.39, 0.54, 0.59, 0.52 and 0.78, p < 0.001), respectively. The Worse MD and thinner RNFLT were most strongly correlated with higher coefficients of Patterns 10 and 12, which represent inferior and superior cupping. The adjusted multiple r (rm) to predict MD separately using the cup shape patterns, ONH related parameters, and 12 clock hour RNFLTs were 0.50, 0.51 and 0.56, respectively, which the model combining the cup shape patterns and ONH related parameters (adjusted rm: 0.60, p < 0.001) outperformed. The model (adjusted rm: 0.63) combining all three types of features outperformed (p < 0.001) the model combining existing RNFLTs and ONH parameters (adjusted rm: 0.59).

Conclusions : The cup shape patterns correlated with established diagnostic parameters and improved the structure-function relationship in glaucoma.

This is a 2021 ARVO Annual Meeting abstract.

 

 

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