June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Deep Learning Predicts Demographic and Clinical Characteristics from Optic Nerve Head OCT Circle and Radial Scans
Author Affiliations & Notes
  • LeAnn Mendoza
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
  • Mark Christopher
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
  • Nicole Brye
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
  • James A Proudfoot
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
  • Akram Belghith
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
  • Christopher Bowd
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
  • Jasmin Rezapour
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
    Department of Ophthalmology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Rhineland-Palatinate, Germany
  • Massimo Antonio Fazio
    Department of Ophthalmology, School of Medicine, University of Alabama-Birmingham, Birmingham, Alabama, United States
  • Michael Henry Goldbaum
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
  • Robert N Weinreb
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
  • Christopher A Girkin
    Department of Ophthalmology, School of Medicine, University of Alabama-Birmingham, Birmingham, Alabama, United States
  • Jeffrey M Liebmann
    Department of Ophthalmology, Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York, United States
  • C Gustavo De Moraes
    Department of Ophthalmology, Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York, United States
  • Linda M Zangwill
    Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
  • Footnotes
    Commercial Relationships   LeAnn Mendoza, None; Mark Christopher, National Eye Institute (F); Nicole Brye, None; James Proudfoot, None; Akram Belghith, None; Christopher Bowd, None; Jasmin Rezapour, German Ophthalmological Society (DOG) (F), German Research Foundation (DFG) (F); Massimo Fazio, EyeSight Foundation of Alabama (F), GmbH (F), Heidelberg Engineering (F), National Eye Institute (F), Research to Prevent Blindness (F); Michael Goldbaum, None; Robert Weinreb, Aerie Pharmaceuticals (C), Allergan (C), Bausch&Lomb (C), Bausch&Lomb (F), Carl Zeiss Meditec (F), Centervue (F), Eyenovia (C), Genentech (F), Heidelberg Engineering (F), Konan Medical (F), Meditec-Zeiss (P), Optos (F), Optovue (F), Toromedes (P), Unity (C); Christopher Girkin, EyeSight Foundation of Alabama (F), GmbH (F), Heidelberg Engineering (F), National Eye Institute (F), Research to Prevent Blindness (F); Jeffrey Liebmann, Alcon (C), Allergan (C), Bausch & Lomb (C), Bausch & Lomb (F), Carl Zeiss Meditec (C), Carl Zeiss Meditec (F), Heidelberg Engineering (C), Heidelberg Engineering (F), National Eye Institute (F), Novartis (F), Optovue (F), Reichert (C), Reichert Technologies (F), Research to Prevent Blindness (F), Valeant Pharmaceuticals (C); C Gustavo De Moraes, Belite (C), Carl Zeiss (C), Galimedix (C), Heidelberg (R), Novartis (C), Perfuse Therapeutics (C), Reichert (C), Topcon (R); Linda Zangwill, Carl Zeiss Meditec (F), Carl Zeiss Meditec (P), GmbH (F), Heidelberg Engineering (F), National Eye Institute (F), Optovue (F), Topcon Medical Systems (F)
  • Footnotes
    Support  EY11008, EY19869, EY14267, EY027510, EY026574, EY029058, EY19869, EY022039, EY027945, EY018926, EY028284, EY026574, EY023704, EY029058, P30EY022589, K99EY030942, Research to Prevent Blindness, German Research Foundation (DFG, research fellowship grant RE 4155/1-1), UCSD School of Medicine Summer Research Fellowship, and participant retention incentive grants in the form of glaucoma medication at no cost from Novartis/Alcon Laboratories Inc, Allergan, Akorn, and Pfizer Inc. Unrestricted grant from Research to Prevent Blindness, New York, New York
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 2120. doi:
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      LeAnn Mendoza, Mark Christopher, Nicole Brye, James A Proudfoot, Akram Belghith, Christopher Bowd, Jasmin Rezapour, Massimo Antonio Fazio, Michael Henry Goldbaum, Robert N Weinreb, Christopher A Girkin, Jeffrey M Liebmann, C Gustavo De Moraes, Linda M Zangwill; Deep Learning Predicts Demographic and Clinical Characteristics from Optic Nerve Head OCT Circle and Radial Scans. Invest. Ophthalmol. Vis. Sci. 2021;62(8):2120.

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

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Abstract

Purpose : To develop deep learning (DL) models for predicting age, sex, race, diabetes diagnosis, hypertension, cardiovascular disease, and axial length from Spectralis optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) circle and radial scans.

Methods : Spectralis OCT circle and radial scans of the optic nerve head (ONH) acquired from healthy subjects, glaucoma suspects and glaucoma patients from the Diagnostic Imaging in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES) were randomly assigned to training (85%), validation (5%) and testing (10%) datasets by patient. DL models were trained on unsegmented circle and radial scans to predict age, sex, race, diabetes diagnosis, hypertension, cardiovascular disease (CVD), and axial length. The circle scan dataset included 52,552 individual B-scans from 1,772 patients. The radial dataset included 111,456 individual B-scans from 730 patients.

Results : The DL models of the best circle and radial scans predicted age with a mean absolute error (MAE (95% CI)) within 5.4 (4.9, 5.9) years and 5.1 (4.5, 5.8) years, respectively and a R2 (95% CI) of 0.73 (0.67, 0.78) and 0.64 (0.49, 0.76), respectively. For Axial length, the circle scan model had a MAE of 0.7 (0.6, 0.9) mm and an R2 of 0.3 (0.2, 0.4), and the radial scan model had a MAE of 0.8 (0.7, 1.0) mm and an R2 of 0.4 (0.2, 0.5). Accuracies (AUROC (95% CI)) for predicting sex in the best circle and radial scans was 0.72 (0.65, 0.79) and 0.68 (0.59, 0.77), respectively. The AUROC for predicting race in the best circle and radial scans was excellent, both 0.96, with 95% CIs of (0.92, 0.98) and (0.91,0.99), respectively. The AUROC for predicting diabetes diagnosis in the best circle and radial scans was 0.65 (0.52, 0.77) and 0.76 (0.64,0.85), respectively. The AUROC for predicting hypertension in circle scans was 0.71(0.59, 0.81) and in radial scans was 0.64 (0.54, 0.73). The AUROC for predicting CVD diagnosis in circle scans was 0.56 (0.47, 0.65) and in radial scans was 0.54 (0.48,0.62).

Conclusions : These results suggest that there are indicators of demographic and clinical characteristics embedded within OCT images showing that DL can infer these characteristics from images of the ONH. With the exception of predictions of diabetes, the DL models of circle scans predicted clinical and demographic features as well or better than radial scans.

This is a 2021 ARVO Annual Meeting abstract.

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