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
In 100 patients the fulcrum axis which is the line connecting the anterior tip of the coracoid and the posterolateral angle of the acromion, was used to position true anteroposterior radiographs of the shoulder. This method was then compared with the conventional radiological technique in a further 100 patients. Three orthopaedic surgeons counted the number of images without overlap between the humeral head and glenoid and calculated the amount of the glenoid surface visible in each radiograph. The analysis was repeated for intraobserver reliability. The learning curves of both techniques were studied. The amount of free visible glenoid space was significantly higher using the fulcrum-axis method (64 vs 31) and the comparable glenoid size increased significantly (8.56 vs 6.47). Thus the accuracy of the anteroposterior radiographs of the shoulder is impaired by using this technique. The intra and interobserver reliability showed a high consistency. No learning curve was observed for either technique.
