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
The aim of this study was to determine whether there is any significant
difference in temporal measurements of pain, function and rates
of re-tear for arthroscopic rotator cuff repair (RCR) patients compared
with those patients undergoing open RCR. This study compared questionnaire- and clinical examination-based
outcomes over two years or longer for two series of patients who
met the inclusion criteria: 200 open RCR and 200 arthroscopic RCR
patients. All surgery was performed by a single surgeon. Objectives
Methods
