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 investigate the differences of open reduction and internal
fixation (ORIF) of complex AO Type C distal radius fractures between
two different models of a single implant type. A total of 136 patients who received either a 2.4 mm (n = 61)
or 3.5 mm (n = 75) distal radius locking compression plate (LCP
DR) using a volar approach were followed over two years. The main
outcome measurements included motion, grip strength, pain, and the
scores of Gartland and Werley, the Short-Form 36 (SF-36) and the
Disabilities of the Arm, Shoulder, and Hand (DASH). Differences
between the treatment groups were evaluated using regression analysis
and the likelihood ratio test with significance based on the Bonferroni
corrected p-value of <
0.003.Objectives
Methods
