June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Performance of Deep Learning Models to Detect Glaucoma Using Unsegmented Radial and Circle OCT Scans of the Optic Nerve Head
Author Affiliations & Notes
  • Mark Christopher
    Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, United States
  • Christopher Bowd
    Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, United States
  • James A Proudfoot
    Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, United States
  • Nicole Brye
    Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, United States
  • Akram Belghith
    Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, United States
  • Michael Henry Goldbaum
    Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, United States
  • Jasmin Rezapour
    Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, United States
    Ophthalmology, University Medical Center Mainz, Mainz, Germany
  • Massimo Antonio Fazio
    School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States
  • Christopher A Girkin
    School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States
  • C Gustavo De Moraes
    Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Dept. of Opthalmology, Columbia University Irving Medical Center, New York, New York, United States
  • Jeffrey M Liebmann
    Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Dept. of Opthalmology, Columbia University Irving Medical Center, New York, New York, United States
  • Robert N Weinreb
    Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, United States
  • Linda M Zangwill
    Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, United States
  • Footnotes
    Commercial Relationships   Mark Christopher, National Eye Institute (F); Christopher Bowd, None; James Proudfoot, None; Nicole Brye, None; Akram Belghith, None; Michael Goldbaum, None; Jasmin Rezapour, German Ophthalmological Society (DOG) (F), German Research Foundation (DFG) (F); Massimo Fazio, EyeSight Foundation of Alabama (F), Heidelberg Engineering (F), National Eye Institute (F), Research to Prevent Blindness (F); Christopher Girkin, EyeSight Foundation of Alabama (F), Heidelberg Engineering (F), National Eye Institute (F), Research to Prevent Blindness (F); C Gustavo De Moraes, Belite (C), Carl Zeiss Meditec (C), Galimedix (C), Heidelberg Engineering (R), Novartis (C), Perfuse Therapeutics (C), Reichert (C), Topcon (R); 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 (F), Research to Prevent Blindess (F), Valeant Pharmaceuticals (C); Robert Weinreb, Aerie Pharmaceuticals (C), Allergan (C), Bausch & Lomb (C), Bausch & Lomb (F), Carl Zeiss Meditec (F), Carl Zeiss Meditec (P), Centervue (F), Equinox (C), Eyenovia (C), Heidelberg Engineering (F), Implantdata (C), Konan (F), National Eye Institute (F), Nicox (C), Optovue (F), Toromedes (P); Linda Zangwill, Carl Zeiss Meditec (F), Carl Zeiss Meditec (P), Heidelberg Engineering (F), National Eye Institute (F), Optovue (F), Topcon (F)
  • Footnotes
    Support  NIH grants: K99 EY030942, P30 EY022589, T32 EY026590, R21 EY027945, EY11008, EY026590, EY022039, EY021818, EY023704, EY029058. German Research Foundation (DFG) RE4155/1-1. German Ophthalmology Society (DOG) grant. Unrestricted grant from Research to Prevent Blindness (New York, NY).
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 1014. doi:
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    • Get Citation

      Mark Christopher, Christopher Bowd, James A Proudfoot, Nicole Brye, Akram Belghith, Michael Henry Goldbaum, Jasmin Rezapour, Massimo Antonio Fazio, Christopher A Girkin, C Gustavo De Moraes, Jeffrey M Liebmann, Robert N Weinreb, Linda M Zangwill; Performance of Deep Learning Models to Detect Glaucoma Using Unsegmented Radial and Circle OCT Scans of the Optic Nerve Head. Invest. Ophthalmol. Vis. Sci. 2021;62(8):1014.

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

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Abstract

Purpose : To evaluate the accuracy of using unsegmented radial and circle OCT scans of the optic nerve head (ONH) in deep learning (DL) models to detect glaucoma and estimate visual field (VF) mean deviation (MD).

Methods : Spectralis ONH radial circle (ONHRC) scans from 192 healthy subjects (330 eyes) and 441 glaucoma patients [DMC1] (712 eyes) provided 2,601 OCTs with 62,424 radial and 7,803 circular B-scans for analysis. The ONH-centered OCTs consisted of 24 equally-spaced radial B-scans and 3 circular B-scans at diameters of 3.5mm, 4.1mm, 4.7mm. VF data consisted of 24-2 testing collected within 180 days of imaging. Subjects were randomly divided into independent training (85%), validation (5%), and test (10%) sets. Individual DL models were trained to distinguish healthy vs. glaucoma eyes and predict VF MD based on unsegmented (1) radial and (2) circular B-scans using Resnet50 models. Diagnostic accuracy was evaluated using area under the receiver operating characteristic curve (AUC) and examined as a function of B-scan type (radial vs. circle), diameter, position, and glaucoma severity. VF estimation was evaluated using R2 and mean absolute error (MAE).

Results : DL models using radial B-scans detected any glaucoma with an AUC (95% CI) of 0.77 (0.63 – 0.87), mild glaucoma (MD >= -6.0 dB) with 0.69 (0.47 – 0.82), and moderate-to-severe glaucoma (MD < -6.0 dB) with 0.86 (0.72 – 0.94). DL models using circular B-scans detected glaucoma with an AUC (95% CI) of 0.84 (0.76 – 0.89), mild glaucoma with 0.75 (0.66 – 0.83), and moderate-to-severe glaucoma with 0.97 (0.94 – 0.98). In detecting glaucoma, circular B-scan diameter had relatively little impact on performance, while radial B-scan position had a larger impact on performance (Figure 1). For predicting VF MD, DL models using circle scans performed better (R2 = 0.83, MAE = 1.8 dB) than models using radial scans (R2 = 0.77, MAE = 1.8 dB).

Conclusions : Circular B-scans outperformed radial in detecting glaucoma and performed comparably in estimating VF damage. However, radial orientation had a substantial impact on glaucoma detection accuracy. DL models that better exploit positional information could help increase accuracy.

This is a 2021 ARVO Annual Meeting abstract.

 

(A) Illustration of the radial and circular B-scans. (B) AUC as a function of circular B-scan diameter. (C) AUC as a function of B-scan orientation.

(A) Illustration of the radial and circular B-scans. (B) AUC as a function of circular B-scan diameter. (C) AUC as a function of B-scan orientation.

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