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In 2016, JAMA published research demonstrating the efficacy of a deep learning algorithm. We were able to train a deep learning neural network to recapitulate the majority decision of 7 or 8 US board certified ophthalmologists in the task of grading for a diabetic retinopathy. The type of deep learning algorithm used to detect diabetic retinopathy in that study is called a Convolutional Neural Network, or CNN. CNNs enable computer systems to analyze and classify data. When applied to images, CNNs can recognize that an image shows a dog rather than a cat. They can recognize the dog whether its a small part or a large part of the picture - size doesnt matter for this technique. It can also classify the dog by breed. CNN systems have also been developed to help clinicians do their work including selecting cellular elements on pathological slides, correctly identifying the spatial orientation of chest radiographs, and, as Dr. Peng mentioned, automatically grading retinal images for diabe
