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Predicting OCT-derived DME grades from fundus photographs using deep learning

Arunachalam Narayanaswamy
Avinash Vaidyanathan Varadarajan
Dr. Paisan Raumviboonsuk
Dr. Peranut Chotcomwongse
Jorge Cuadros
Lily Hao Yi Peng
Pearse Keane
Subhashini Venugopalan
Nature Communications (2020)
Google Scholar


Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF therapies, it has become increasingly important to detect center-involved DME (ci-DME). However, ci-DME is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites. Instead, screening programs rely on the detection of hard exudates as a proxy for DME on color fundus photographs, but this often results in a fair number of false positive and false negative calls. We trained a deep learning model to use color fundus images to directly predict grades derived from OCT exams for DME. Our OCT-based model had an AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, the ophthalmology graders had sensitivities ranging from 82%-85% and specificities ranging from 44%-50%. These metrics correspond to a PPV of 61% (95% CI: 56%-66%) for the OCT-based algorithm and a range of 36-38% (95% CI ranging from 33% -42%) for ophthalmologists. In addition, we used multiple attention techniques to explain how the model is making its prediction. The ability of deep learning algorithms to make clinically relevant predictions that generally requires sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging.