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 optimal choice of management for proximal humerus fractures (PHFs) has been increasingly discussed in the literature, and this work aimed to answer the following questions: 1) what are the incidence rates of PHF in the geriatric population in the USA; 2) what is the mortality rate after PHF in the elderly population, specifically for distinct treatment procedures; and 3) what factors influence the mortality rate? PHFs occurring between 1 January 2009 and 31 December 2019 were identified from the Medicare physician service records. Incidence rates were determined, mortality rates were calculated, and semiparametric Cox regression was applied, incorporating 23 demographic, clinical, and socioeconomic covariates, to compare the mortality risk between treatments.Aims
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