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To evaluate the performance of a deep convolutional neural network (DCNN) in detecting and classifying distal radius fractures, metal, and cast on radiographs using labels based on radiology reports. The secondary aim was to evaluate the effect of the training set size on the algorithm's performance.

european radiology

AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size