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However, there continues to be a need for automated accurate cone photoreceptor recognition in pictures of infection. Here, we apply an open-source convolutional neural network (CNN) to automatically identify cones in photos of choroideremia (CHM). We further compare the outcome to the repeatability and reliability of manual cone identifications in CHM. We used split-detection adaptive optics scanning laser ophthalmoscopy to image the internal section cone mosaic of 17 customers with CHM.
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