The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support. The dataset consisted of 26,121 anonymized anterior-posterior (AP) and lateral standard view radiographs of the wrist, with and without DRF. The convolutional neural network (CNN) model was trained to detect the presence of a DRF by comparing the radiographs containing a fracture to the inconspicuous ones. A total of 11 physicians (six surgeons in training and five hand surgeons) assessed 200 pairs of randomly selected digital radiographs of the wrist (AP and lateral) for the presence of a DRF. The same images were first evaluated without, and then with, the support of the CNN model, and the diagnostic accuracy of the two methods was compared.Aims
Methods
To evaluate if union of clavicle fractures can be predicted at six weeks post-injury by the presence of bridging callus on ultrasound. Adult patients managed nonoperatively with a displaced mid-shaft clavicle were recruited prospectively. Ultrasound evaluation of the fracture was undertaken to determine if sonographic bridging callus was present. Clinical risk factors at six weeks were used to stratify patients at high risk of nonunion with a combination of Quick Disabilities of the Arm, Shoulder and Hand questionnaire (QuickDASH) ≥ 40, fracture movement on examination, or absence of callus on radiograph.Aims
Methods
