Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments

Background and purpose — Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identifying the regions of interest, to detect distal radius fractures (DRFs) on anterior–posterior (AP) wrist radiographs. Patients and methods — 2,340 AP wrist radiographs from 2,340 patients were enrolled in this study. We trained the CNN to analyze wrist radiographs in the dataset. Feasibility of the object detection algorithm was evaluated by intersection of the union (IOU). The diagnostic performance of the network was measured by area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity, and Youden Index; the results were compared with those of medical professional groups. Results — The object detection model achieved a high average IOU, and none of the IOUs had a value less than 0.5. The AUC of the CNN for this test was 0.96. The network had better performance in distinguishing images with DRFs from normal images compared with a group of radiologists in terms of the accuracy, sensitivity, specificity, and Youden Index. The network presented a similar diagnostic performance to that of the orthopedists in terms of these variables. Interpretation — The network exhibited a diagnostic ability similar to that of the orthopedists and a performance superior to that of the radiologists in distinguishing AP wrist radiographs with DRFs from normal images under limited conditions. Further studies are required to determine the feasibility of applying our method as an auxiliary in clinical practice under extended conditions.