Diabetic retinopathy is the leading cause of blindness and the most common diabetic eye disease. There are approximately 93 million people with diabetic retinopathy worldwide. While the exact mechanism by which diabetes causes retinopathy is unclear, several clinical features are used to accurately grade and classify diabetic retinopathy. The following features can commonly be identified in fundus images:
Adapted from COMS Grading Scheme
It is apparent that many of these features can't easily be quantified and their severity is also determined by the presence and relative positions of other clinical features. While image processing algorithms could be developed for each individual clinical feature, the correlations between the clinical features and their spatial positions cannot easily be accounted for by stacking individual image processing algorithms. Given the complexity of this image analysis task, we used the SemanticMD Compute Cloud to train a deep learning network for classifying images with diabetic retinopathy.
The first step was to use SemanticMD Annotate Cloud to manage and annotate fundus images using a web-based platform. This enabled a team of trained imaging technicians to quickly process hundreds of fundus images within days. Once the data was organized and categorized, SemanticMD Compute Cloud was used to ingest the annotated fundus images and produce a web demo of a production-ready deep learning model. We host state-of-the-art deep learning libraries such as Caffe and Keras (Theano wrapper) so our models are continually up-to-date with the latest research.
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Dr. Eddy Philippe, Ophthalmic Image Analysis Consultant
"SemanticMD Cloud uses the latest advancements in deep learning and web technologies to accelerate R&D and develop products - fast."