June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Equitable Deep Learning for Glaucoma Progression Forecasting
Author Affiliations & Notes
  • Mengyu Wang
    Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Tobias Elze
    Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of 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
  • Lucy Q. Shen
    Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Saber Kazeminasab Hashemabad
    Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Mohammad Eslami
    Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Min Shi
    Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Yu Tian
    Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Yan Luo
    Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Mengyu Wang Genentech, Code F (Financial Support); Tobias Elze Genentech, Code F (Financial Support); Louis Pasquale Eyenovia-Advisory Board Member, Twenty-Twenty and Skye Biosciences, Code C (Consultant/Contractor); Lucy Shen Firecyte Therapeutics and Abbvie, Code C (Consultant/Contractor); Saber Kazeminasab Hashemabad None; Mohammad Eslami None; Min Shi None; Yu Tian None; Yan Luo None
  • Footnotes
    Support  This work was supported by NIH R00 EY028631, Research To Prevent Blindness International Research Collaborators Award, Alcon Young Investigator Grant, NIH R21 EY030631, NIH R01 EY030575, NIH R01 EY015473, NIH R21 EY031725, NIH R01 EY033005 and NIH P30 EY003790.
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 340. doi:
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    • Get Citation

      Mengyu Wang, Tobias Elze, Louis R Pasquale, Lucy Q. Shen, Saber Kazeminasab Hashemabad, Mohammad Eslami, Min Shi, Yu Tian, Yan Luo; Equitable Deep Learning for Glaucoma Progression Forecasting. Invest. Ophthalmol. Vis. Sci. 2023;64(8):340.

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

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Abstract

Purpose : Blacks have more than doubled glaucoma prevalence than other races, while deep learning performance for Blacks is typically worse due to data scarcity for minorities. We aim to develop an equitable deep learning model for glaucoma progression forecasting.

Methods : We selected reliable optical coherence tomography (OCT) scans from patients with at least 5 reliable 24-2 VFs over at least 4 years. We calculated 4 VF progression outcomes: (1) mean deviation (MD) progression: MD slope < 0 and p-value < 0.05; (2) VF index (VFI) progression: VFI slope < 0 and p-value < 0.05; (3) total deviation (TD) pointwise progression: at least 3 TD locations with TD slope ≤ -1 dB/year and p-value < 0.05; (4) MD fast progression: MD slope ≤ -1 dB/year and p-value < 0.05. We developed an equitable deep learning model guided by a fair loss to predict VF progression using baseline retinal nerve fiber layer thickness maps (Figure 1). The fair loss reweighted training loss based on effective sample sizes for each race. Our models were trained and tested using 2/3 and 1/3 of the dataset with patient-level separation, respectively. T-test with bootstrapping sampling was used to compare the performance of our models with and without fair loss guidance measured by the area under the receiver operating characteristic curve (AUC).

Results : 14,928 OCT scans from 4,726 eyes of 2,850 patients (age: 62.1 ± 12.8 years; MD: -3.0 ± 4.1 dB; White: 76.9%; Black: 14.6%; Asian: 8.5%) were included. The progression prevalence for MD progression, VFI progression, TD pointwise progression, and MD fast progression were 9.8%, 10.9%, 11.3%, and 2.5% with a median follow-time of 6.6 years, respectively. For MD, VFI and TD pointwise progressions, AUCs for Asians (Figure 2) increased from 0.68 to 0.75, 0.76 to 0.78 and 0.80 to 0.82 (p < 0.001), respectively. For all four progression outcomes, AUCs for Blacks increased from 0.82 to 0.84, 0.76 to 0.86, 0.84 to 0.89 and 0.83 to 0.89 (p < 0.001). AUC for Whites improved for MD progression prediction from 0.80 to 0.83 and stayed uncompromised for the other three progression outcomes. Our model with fair loss learned features with greater distances between racial groups than the model without fair loss.

Conclusions : Our equitable deep learning model improved performance for Asians and Blacks and did not compromise performance for Whites, which can potentially reduce health disparity in medical artificial intelligence.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

 

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