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
Fibrinolysis plays a key transition step from haematoma formation to angiogenesis and fracture healing. Low-magnitude high-frequency vibration (LMHFV) is a non-invasive biophysical modality proven to enhance fibrinolytic factors. This study investigates the effect of LMHFV on fibrinolysis in a clinically relevant animal model to accelerate osteoporotic fracture healing. A total of 144 rats were randomized to four groups: sham control; sham and LMHFV; ovariectomized (OVX); and ovariectomized and LMHFV (OVX-VT). Fibrinolytic potential was evaluated by quantifying fibrin, tissue plasminogen activator (tPA), and plasminogen activator inhibitor-1 (PAI-1) along with healing outcomes at three days, one week, two weeks, and six weeks post-fracture.Aims
Methods
