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Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Author

Listed:
  • Avinash V. Varadarajan

    (Google Health, Google)

  • Pinal Bavishi

    (Google Health, Google)

  • Paisan Ruamviboonsuk

    (Rangsit University)

  • Peranut Chotcomwongse

    (Rangsit University)

  • Subhashini Venugopalan

    (Google Research, Google, Mountain View)

  • Arunachalam Narayanaswamy

    (Google Research, Google, Mountain View)

  • Jorge Cuadros

    (EyePACS LLC)

  • Kuniyoshi Kanai

    (University of California)

  • George Bresnick

    (EyePACS LLC)

  • Mongkol Tadarati

    (Rangsit University)

  • Sukhum Silpa-archa

    (Rangsit University)

  • Jirawut Limwattanayingyong

    (Rangsit University)

  • Variya Nganthavee

    (Rangsit University)

  • Joseph R. Ledsam

    (Deepmind)

  • Pearse A. Keane

    (Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology)

  • Greg S. Corrado

    (Google Health, Google)

  • Lily Peng

    (Google Health, Google)

  • Dale R. Webster

    (Google Health, Google)

Abstract

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC–AUC of 0.89 (95% CI: 0.87–0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82–85%), but only half the specificity (45–50%, p

Suggested Citation

  • Avinash V. Varadarajan & Pinal Bavishi & Paisan Ruamviboonsuk & Peranut Chotcomwongse & Subhashini Venugopalan & Arunachalam Narayanaswamy & Jorge Cuadros & Kuniyoshi Kanai & George Bresnick & Mongkol, 2020. "Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-13922-8
    DOI: 10.1038/s41467-019-13922-8
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