June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Individualized deep learning auto-encoder (DL-AE)-based OCT deviation maps for improved glaucoma progression detection
Author Affiliations & Notes
  • Christopher Bowd
    Hamilton Glaucoma Center, Shiley Eye Institute,The Viterbi Family Department of Ophthalmology, University of California, San Diego, San Diego, California, United States
  • Akram Belghith
    Hamilton Glaucoma Center, Shiley Eye Institute,The Viterbi Family Department of Ophthalmology, University of California, San Diego, San Diego, California, United States
  • Mark Christopher
    Hamilton Glaucoma Center, Shiley Eye Institute,The Viterbi Family Department of Ophthalmology, University of California, San Diego, San Diego, California, United States
  • Michael Henry Goldbaum
    Hamilton Glaucoma Center, Shiley Eye Institute,The Viterbi Family Department of Ophthalmology, University of California, San Diego, San Diego, California, United States
  • 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
  • Jeffrey M Liebmann
    Bernard and Shirlee Brown Glaucoma Research Laboratory, Harkness Eye Institute, Columbia University Medical Center, New York, New York, United States
  • C Gustavo De Moraes
    Bernard and Shirlee Brown Glaucoma Research Laboratory, Harkness Eye Institute, Columbia University Medical Center, New York, New York, United States
  • Robert N Weinreb
    Hamilton Glaucoma Center, Shiley Eye Institute,The Viterbi Family Department of Ophthalmology, University of California, San Diego, San Diego, California, United States
  • Linda M Zangwill
    Hamilton Glaucoma Center, Shiley Eye Institute,The Viterbi Family Department of Ophthalmology, University of California, San Diego, San Diego, California, United States
  • Footnotes
    Commercial Relationships   Christopher Bowd, None; Akram Belghith, None; Mark Christopher, None; Michael Goldbaum, None; Massimo Fazio, Heidelberg Engineering GmbH (F); Christopher Girkin, Heidelberg Engineering GmbH (F); Jeffrey Liebmann, Aerie Pharmaceuticals Inc. (C), Alcon Inc. (C), Allergan Inc. (C), Bausch & Lomb (F), Bausch & Lomb Inc. (C), Carl Zeiss Meditec Inc. (F), Carl Zeiss Meditec Inc. (C), Eyenovia Inc. (C), Galimedix Therapeutics Inc. (C), Heidelberg Engineering GmbH (F), Heidelberg Engineering GmbH (C), Novartis Parmaceuticals (C), Optovue Inc. (F), Reichert Inc. (F), Reichert Inc. (C), Topcon (F); C Gustavo De Moraes, Belite Bio (C), Carl Zeiss Meditec inc. (C), Galimedix Therapeutics Inc. (C), Heidelberg Engineering GmbH (R), Novartis (C), Perfuse Therapeutics Inc. (C), Topcon Medical Systems Inc. (R); Robert Weinreb, Aerie Pharmaceuticals Inc. (C), Allergan (C), Bausch & Lomb (C), Bausch & Lomb (F), Carl Zeiss Meditec Inc. (F), Carl Zeiss Meditec Inc. (P), CenterVue (F), Eyenovia Inc. (C), Konan Medical (F), Optovue Inc. (F), Toromedes Inc. (P); Linda Zangwill, Carl Zeiss Meditec Inc. (F), Carl Zeiss Meditec Inc. (P), Heidelberg Engineering GmbH (F), Heidelberg Engineering GmbH (R), Optovue Inc. (F), Topcon Medical Systems Inc. (F)
  • Footnotes
    Support  NIH Grants R21EY027945, T32EY026590, R01EY026574, R01EY011008, R01EY019869, P30EY022589, EyeSight Foundation of Alabama, Edith C. Blum Research Fund of the New York Glaucoma Research Institute, Research to Prevent Blindness and participant retention incentive grants in the form of glaucoma medication at no cost from Novartis/Alcon Laboratories Inc, Allergan, Akorn, and Pfizer Inc.
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 4536. doi:
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    • Get Citation

      Christopher Bowd, Akram Belghith, Mark Christopher, Michael Henry Goldbaum, Massimo Antonio Fazio, Christopher A Girkin, Jeffrey M Liebmann, C Gustavo De Moraes, Robert N Weinreb, Linda M Zangwill; Individualized deep learning auto-encoder (DL-AE)-based OCT deviation maps for improved glaucoma progression detection. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4536.

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

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Abstract

Purpose : To compare individualized, eye-specific Spectralis OCT regional RNFL change detection maps developed using unsupervised DL-AE strategies to circumpapillary RNFL thickness change over time for detection of glaucomatous progression.

Methods : Forty-four progressing glaucoma eyes (by stereophotograph assessment),189 non-progressing glaucoma eyes (by stereophotograph assessment) and 109 stable healthy eyes from the Diagnostic Imaging in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES) were followed over 3 to 5 years with 4 to 10 visits using Spectralis OCT. Fifty stable glaucoma eyes (tested weekly for five weeks) were used to train DL-AEs to identify regional change greater than measurement variability. The San Diego Automated Layer Segmentation Algorithm (SALSA) was used to automatically segment the RNFL layer from raw 3-D OCT images. For each longitudinal series, DL-AEs were used to generate individualized eye-based change detection maps by identifying RNFL regions of likely progression and no change. Sensitivities and specificities for detecting regional change over time and rates of change over time were compared for the DL-AE model and circumpapillary RNFL circular thickness maps (obtained within a 2.22 mm to 3.45 mm annulus centered on the optic nerve).

Results : Sensitivity for detecting change in progressing glaucoma eyes was greater for DL-AE maps than RNFL circular thickness maps (0.90 and 0.63, respectively), while specificity for detecting non-progression in non-progressing glaucoma eyes was similar (0.92 and 0.93, respectively); 40% more progressing eyes where identified using DL-AE maps compared to RNFL circular thickness maps. Mean (95% CI) rates of change in DL-AE map regions of likely progression were significantly faster than for RNFL circular thickness maps in progressing glaucoma eyes [-1.28 µm/yr (-1.38 µm/yr, -1.15 µm/yr) vs. -0.83 µm/yr (-0.93 µm/yr, -0.72 µm/yr)], non-progressing glaucoma eyes [-1.03 µm/yr (-1.21 µm/yr, -0.93 µm/yr) vs. -0.78 µm/yr (-0.88 µm/yr, -0.67 µm/yr)] and healthy eyes [-0.83 µm/yr (-0.92 µm/yr, -0.73 µm/yr) vs. -0.65 µm/yr (-0.74 µm/yr, -0.56 µm/yr)].

Conclusions : By tailoring analysis to the individual patient, individualized regions of interest identified using unsupervised deep learning auto-encoder analysis of OCT images show promise for improving assessment of glaucomatous progression.

This is a 2020 ARVO Annual Meeting abstract.

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