학술논문
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Document Type
article
Author
Varadarajan, Avinash V; Bavishi, Pinal; Ruamviboonsuk, Paisan; Chotcomwongse, Peranut; Venugopalan, Subhashini; Narayanaswamy, Arunachalam; Cuadros, Jorge; Kanai, Kuniyoshi; Bresnick, George; Tadarati, Mongkol; Silpa-archa, Sukhum; Limwattanayingyong, Jirawut; Nganthavee, Variya; Ledsam, Joseph R; Keane, Pearse A; Corrado, Greg S; Peng, Lily; Webster, Dale R
Source
Nature Communications. 11(1)
Subject
Language
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