Nature Communications (Jan 2020)

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

  • Avinash V. Varadarajan,
  • Pinal Bavishi,
  • Paisan Ruamviboonsuk,
  • Peranut Chotcomwongse,
  • Subhashini Venugopalan,
  • Arunachalam Narayanaswamy,
  • Jorge Cuadros,
  • Kuniyoshi Kanai,
  • George Bresnick,
  • Mongkol Tadarati,
  • Sukhum Silpa-archa,
  • Jirawut Limwattanayingyong,
  • Variya Nganthavee,
  • Joseph R. Ledsam,
  • Pearse A. Keane,
  • Greg S. Corrado,
  • Lily Peng,
  • Dale R. Webster

DOI
https://doi.org/10.1038/s41467-019-13922-8
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 8

Abstract

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Diabetic eye disease is a cause of preventable blindness and accurate and timely referral of patients with diabetic macular edema is important to start treatment. Here the authors present a deep learning model that can predict the presence of diabetic macular edema from color fundus photographs with superior specificity and positive predictive value compared to retinal specialists.