July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Glaucoma monitoring using an artificial intelligence enabled map
Author Affiliations & Notes
  • Siamak Yousefi
    Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States
    Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Tobias Elze
    Ophthalmology, Harvard University, Boston, Massachusetts, United States
  • Louis Pasquale
    Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Michael V Boland
    Ophthalmology, Johns Hopkins University, Baltimore, Maryland, United States
  • Footnotes
    Commercial Relationships   Siamak Yousefi, Research to Prevent Blindness (F); Tobias Elze, BrightFocus (F), Lions Foundation (F), Research to Prevent Blindness (F); Louis Pasquale, None; Michael Boland, None
  • Footnotes
    Support  Stein Innovation Award from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2439. doi:
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    • Get Citation

      Siamak Yousefi, Tobias Elze, Louis Pasquale, Michael V Boland; Glaucoma monitoring using an artificial intelligence enabled map. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2439.

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

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Abstract

Purpose : To develop an artificial map for monitoring glaucoma progression using manifold learning and unsupervised clustering

Methods : We acquired 31,591 VF tests from 8,077 subjects using the Humphrey Field Analyzer. We selected 13,231 VFs at the baseline visit for training the machine learning framework. We first applied principal component analysis (PCA) to linearly reduce the number of dimensions of the VFs. We then applied manifold learning using t-distributed stochastic neighbor embedding (tSNE) to identify VFs with similar local characteristics on the tSNE manifold. We then developed unsupervised clustering to identify clusters of eyes densely located on the tSNE map (see Fig. 1). These clusters represent eyes at different stages of glaucoma as well as different patterns of VF loss (see Fig. 2). We used this map as a reference artificial screen to stage and monitor glaucoma. Finally, we evaluated the method using subjective visualization of clusters and objective validation using mean deviation (MD) and pattern standard deviation (PSD).

Results : The mean age of subjects selected for training the machine learning framework was 60.6 years (SD= 17.9). Sample eyes with confirmed progression from an independent datasets with over eight years of follow-up with 16 VF tests consistently followed the direction of glaucoma progression on the artificial tSNE map (subjective evaluation). Other sample eyes with confirmed non-progression (eyes tested once a week for 10 consecutive weeks) consistently represented no progression on the artificial tSNE map (subjective evaluation). The artificial tSNE map showed a greater dynamic range (from normal to advanced glaucoma) compared with the MD versus PSD map (see Fig. 1).

Conclusions : We demonstrate that glaucoma functional severity and progression can be readily monitored using an artificial-intelligence-enabled map. The proposed tool could be highly useful in clinical practice and glaucoma research for staging and monitoring glaucoma on a user-friendly map.

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

 

Figure 1. MD versus PSD of the VFs. Top left: scatter plot of the MD versus PSD, bottom-left: clusters identified based on MD and PSD, top right: scatter plot of the VFs on the tSNE map, bottom-right: clusters identified based on tSNE parameters

Figure 1. MD versus PSD of the VFs. Top left: scatter plot of the MD versus PSD, bottom-left: clusters identified based on MD and PSD, top right: scatter plot of the VFs on the tSNE map, bottom-right: clusters identified based on tSNE parameters

 

Figure 2. Severity of the clusters. Top: mean deviation, middle: mean total deviation at the inferior hemifield, and bottom: mean total deviation at the superior hemifield of the clusters.

Figure 2. Severity of the clusters. Top: mean deviation, middle: mean total deviation at the inferior hemifield, and bottom: mean total deviation at the superior hemifield of the clusters.

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