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Distal radius fractures are the most common fractures of the upper extremityin humans. As such, they account for a significant portion of the injuries thatpresent to emergency rooms and clinics throughout the world. We trained aFaster R-CNN, a machine vision neural network for object detection, to identifyand locate distal radius fractures in anteroposterior X-ray images. We achievedan accuracy of 96\% in identifying fractures and mean Average Precision, mAP,of 0.866. This is significantly more accurate than the detection achieved byphysicians and radiologists. These results were obtained by training the deeplearning network with only 38 original images of anteroposterior hands X-rayimages with fractures. This opens the possibility to detect with this type ofneural network rare diseases or rare symptoms of common diseases , where only asmall set of diagnosed X-ray images could be collected for each disease.

Detection of distal radius fractures trained by a small set of X-ray images and Faster R-CNN