This study aimed to develop and evaluate a convolutional neural network for identifying scaphoid fractures on radiographs. A dataset of 1918 wrist radiographs (600 patients) was taken from an orthopaedic referral centre between 2010 to 2020. A YOLOv3 and a MobileNetV3 convolutional neural network were trained for scaphoid detection and fracture classification, respectively. The diagnostic performance of the convolutional neural network was compared with the majority decision of four hand surgeons. The convolutional neural network achieved a sensitivity of 82% and specificity of 94%, with an area under the receiver operating characteristic of 92%, whereas the surgeons achieved a sensitivity of 76% and specificity of 96%. The comparison indicated that the convolutional neural network’s performance was similar to the majority vote of surgeons. It further revealed that convolutional neural network could be used in identifying scaphoid fractures on radiographs reliably, and has potential to achieve the expert-level performance. Level of evidence: III
We investigated abnormal MRI findings of the triangular fibrocartilage complex in 154 asymptomatic volunteers (21–79 years). Except prevalence, we focused on the morphological features of abnormal signals in relation to age. The majority of full-thickness tears were located in the articular disc (63 participants). The incidence of disc perforation with characteristics of ulnar impaction syndrome increased significantly with age. Asymptomatic full-thickness tears of the ulnar attachment were found in ten participants (seven over 60 years old). The proximal and distal laminae of the ulnar attachment could not be differentiated in 36 participants. In conclusion, MRI is of limited value for the elderly in diagnosing triangular fibrocartilage disorders. For young subjects, MRI is still valuable, especially in diagnosing ulnar detachment, although the ability to distinguish between proximal and distal laminae remains questionable. Disc perforations in volunteers mimicked ulnar impaction syndrome, therefore age, clinical signs and other factors should also be considered in clinical diagnosis. Level of evidence: III
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