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 () and Dale R. Webster
Additional contact information
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

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: 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

Date: 2020
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DOI: 10.1038/s41467-019-13922-8

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