July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Deep learning prediction of progression to late age-related macular degeneration in the Age-Related Eye Disease Study (AREDS) using deep feature extraction and survival analysis
Author Affiliations & Notes
  • Tiarnan D L Keenan
    Division of Epidemiology and Clinical Applications, National Eye Institute, Bethesda, Maryland, United States
  • Yifan Peng
    National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland, United States
  • Qingyu Chen
    National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland, United States
  • Elvira Agron
    Division of Epidemiology and Clinical Applications, National Eye Institute, Bethesda, Maryland, United States
  • Wai T Wong
    Unit on Neuron-Glia Interactions in Retinal Disease, National Eye Institute, National Institutes of Health, Bethesda, Maryland, United States
  • Zhiyong Lu
    National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland, United States
  • Emily Y Chew
    Division of Epidemiology and Clinical Applications, National Eye Institute, Bethesda, Maryland, United States
  • Footnotes
    Commercial Relationships   Tiarnan Keenan, None; Yifan Peng, None; Qingyu Chen, None; Elvira Agron, None; Wai Wong, None; Zhiyong Lu, None; Emily Chew, None
  • Footnotes
    Support  Supported by the intramural program funds and contracts from the National Center for Biotechnology Information/National Library of Medicine/National Institutes of Health, the National Eye Institute/National Institutes of Health, Department of Health and Human Services, Bethesda Maryland (contract HHS-N-260-2005-00007-C; ADB contract NO1-EY-5-0007). Funds were generously contributed to these contracts by the following NIH institutes: Office of Dietary Supplements, National Center for Complementary and Alternative Medicine; National Institute on Aging; National Heart, Lung, and Blood Institute, and National Institute of Neurological Disorders and Stroke.
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1491. doi:https://doi.org/
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      Tiarnan D L Keenan, Yifan Peng, Qingyu Chen, Elvira Agron, Wai T Wong, Zhiyong Lu, Emily Y Chew; Deep learning prediction of progression to late age-related macular degeneration in the Age-Related Eye Disease Study (AREDS) using deep feature extraction and survival analysis. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1491. doi: https://doi.org/.

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

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Abstract

Purpose : Previous deep learning (DL) attempts at predicting progression to late age-related macular degeneration (AMD) from color fundus photographs (CFP) have relied on ‘hand-crafted’ image features (drusen/pigmentary changes) and published 5-year risk estimates. The purpose was to develop, train, and validate a DL model for predicting progression from bilateral CFP using survival analysis and deep feature extraction. Deep feature extraction allows the unconstrained model to derive and weight multiple predictive image features, including complex features potentially absent from human grading systems.

Methods : A DL survival (Cox proportional-hazards) model was trained to predict probability of progression to late AMD (neovascular AMD or central GA) at patient level for AREDS participants (55-80y, no AMD to non-advanced AMD at baseline). Four approaches were compared (see Figure). All models had demographic information as input. Based on bilateral CFP, Models 2-4 also received:
Model 2: grading for drusen/pigmentary changes by human retinal specialists;
Model 3: drusen/pigmentary changes predicted by DL (‘DeepSeeNet’, Peng et al., Ophthalmol. 2018);
Model 4: deep features extracted from DeepSeeNet fully-connected layer.
Each model was trained and tested on the same 3,535 and 710 participants, respectively (mean follow-up 8.2y). The primary outcome was concordance index (c-index, identical to AUC).

Results : The c-index of each model was 0.703 (1), 0.718 (2), 0.884 (3), and 0.895 (4). The models using DL had substantially superior performance. Deep feature extraction (4) was superior to traditional features (3). In model 4, the most predictive characteristics were the imaging features (i.e. ranked above age/smoking).

Conclusions : A DL model that combined deep feature extraction and survival analysis provided accurate time-based predictions of progression to late AMD. Deep feature extraction achieved superior performance to traditional features (assessed by retinal specialists or predicted by DL). By combining survival analysis with a deeper characterization of fundus images, this approach has advantages over previous attempts. This demonstrates the potential of deep feature-based biomarkers and survival analysis in improving AMD risk models.

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

 

Figure. Survival analysis models showing model inputs.

Figure. Survival analysis models showing model inputs.

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