June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Detection of Glaucoma Progression from Retinal Nerve Fiber Layer Thickness Measurements Using Machine Learning
Xiaoqin Huang; Sidharth Mahotra; Tobias Elze; Mengyu Wang; Michael V. Boland; Louis Pasquale; Juleke Eugenie Anne Majoor; Hans Lemij; Kouros Nouri-Mahdavi; Chris A Johnson; Siamak Yousefi
Author Affiliations & Notes
  • Xiaoqin Huang
    Department of Ophthalmology, The University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Sidharth Mahotra
    Department of Ophthalmology, The University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Tobias Elze
    Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Mengyu Wang
    Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Michael V. Boland
    Department of Ophthalmology, Massachusetts Eye and Ear, Boston, Massachusetts, United States
  • Louis Pasquale
    Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Juleke Eugenie Anne Majoor
    Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, Rotterdam, Netherlands
  • Hans Lemij
    Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, Rotterdam, Netherlands
  • Kouros Nouri-Mahdavi
    Stein Eye Institute, University of California Los Angeles, Los Angeles, California, United States
  • Chris A Johnson
    Department of Ophthalmology and Visual Sciences, The University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
  • Siamak Yousefi
    Department of Ophthalmology, The University of Tennessee Health Science Center, Memphis, Tennessee, United States
    Department of Genetics, Genomics, and Informatics, The University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Footnotes
    Commercial Relationships   Xiaoqin Huang, None; Sidharth Mahotra, None; Tobias Elze, None; Mengyu Wang, None; Michael V. Boland, None; Louis Pasquale, None; Juleke Majoor, None; Hans Lemij, None; Kouros Nouri-Mahdavi, None; Chris Johnson, None; Siamak Yousefi, None
  • Footnotes
    Support  NIH EY030142, NIH EY031725
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 1005. doi:
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      Xiaoqin Huang, Sidharth Mahotra, Tobias Elze, Mengyu Wang, Michael V. Boland, Louis Pasquale, Juleke Eugenie Anne Majoor, Hans Lemij, Kouros Nouri-Mahdavi, Chris A Johnson, Siamak Yousefi; Detection of Glaucoma Progression from Retinal Nerve Fiber Layer Thickness Measurements Using Machine Learning. Invest. Ophthalmol. Vis. Sci. 2021;62(8):1005.

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Abstract

Purpose : To develop a machine learning model for detecting glaucoma progression from retinal nerve fiber layer (RNFL) thickness measurements acquired with optical coherence tomography (OCT).

Methods : We developed an unsupervised Gaussian mixture model with an expectation maximization (GEM) framework to identify clusters with similar RNFL thickness patterns in circular OCT scans (768 A-scans) from 691 eyes of 691 patients. We then identified the top prevalent eigenvectors (patterns) of the clusters representing eyes in the mild and advanced stages of glaucoma. We selected the slopes across these eigenvectors such that 95% of 2540 eyes in a separate longitudinal stable dataset (~9 follow-up visits) were not progressing. We used the selected slopes to test the model on a third longitudinal dataset with 254 eyes (~9 visits) and compared the detection rate of the proposed machine learning model with linear regression of RNFL summary parameters.

Results : Machine learning discovered three clusters within 691 RNFL thickness measurements of 691 eyes (Fig. 1). A total of 12 patterns of RNFL loss (eigenvectors) were prevalent and accounted for about 77% of the total variability in the data (Fig. 2). The machine learning model detected glaucoma progression in 38.6% of the eyes tested, while average RNFL in global, superior, and inferior hemifields detected progression in 15.8%,13.0% and 15.0%, of the eyes, respectively.

Conclusions : The proposed machine learning construct identified a higher proportion of structural progression based on RNFL thickness measurements than linear regression of summary parameters. Additionally, the model identifies the major patterns of RNFL loss, which may aid clinicians in better monitoring of glaucoma subjects.

This is a 2021 ARVO Annual Meeting abstract.

 

Figure 1. Average RNFL thickness of three clusters that were identified using unsupervised Gaussian mixture model with expectation maximization (GEM) framework.
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Figure 1. Average RNFL thickness of three clusters that were identified using unsupervised Gaussian mixture model with expectation maximization (GEM) framework.

Figure 1. Average RNFL thickness of three clusters that were identified using unsupervised Gaussian mixture model with expectation maximization (GEM) framework.

Figure 1. Average RNFL thickness of three clusters that were identified using unsupervised Gaussian mixture model with expectation maximization (GEM) framework.
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Figure 2. Patterns of RNFL loss identified using the machine learning approach.
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Figure 2. Patterns of RNFL loss identified using the machine learning approach.

Figure 2. Patterns of RNFL loss identified using the machine learning approach.

Figure 2. Patterns of RNFL loss identified using the machine learning approach.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Copyright © 2025 Association for Research in Vision and Ophthalmology.
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