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

Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular e...

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Bibliographic Details
Published in:arXiv.org 2019-07
Main Authors: Varadarajan, Avinash, Pinal Bavishi, Raumviboonsuk, Paisan, Chotcomwongse, Peranut, Venugopalan, Subhashini, Arunachalam Narayanaswamy, Cuadros, Jorge, Kanai, Kuniyoshi, Bresnick, George, Tadarati, Mongkol, Silpa-archa, Sukhum, Limwattanayingyong, Jirawut, Nganthavee, Variya, Ledsam, Joe, Keane, Pearse A, Corrado, Greg S, Peng, Lily, Webster, Dale R
Format: Article
Language:English
Subjects:
Algorithms
Blindness
Color
Deep learning
Diabetes
Edema
Exudation
Eye diseases
Growth factors
Mathematical models
Medical imaging
Optical Coherence Tomography
Predictions
Screening
Sensitivity
Tomography
Workflow
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Summary:Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates in color fundus photographs as a proxy for DME, often resulting in high false positive or false negative calls. To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME. Our model had an ROC-AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal specialists had similar sensitivities (82-85%), but only half the specificity (45-50%, p
ISSN:2331-8422
DOI:10.48550/arxiv.1810.10342