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

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?

SUBMITTER: Varadarajan AV 

PROVIDER: S-EPMC6949287 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature