The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs. The CNN was trained and tested on radiographs sourced from 11 hospitals in Australia and externally validated on radiographs from the Netherlands. Each radiograph was paired with corresponding CT scans to serve as the reference standard based on dual independent evaluation by trained researchers and attending orthopaedic surgeons. Presence of a fracture, classification (non- to minimally displaced; two-part, multipart, and glenohumeral dislocation), and four characteristics were determined on 2D and 3D CT scans and subsequently allocated to each series of radiographs. Fracture characteristics included greater tuberosity displacement ≥ 1 cm, NSA ≤ 100°, shaft translation (0% to < 75%, 75% to 95%, > 95%), and the extent of articular involvement (0% to < 15%, 15% to 35%, or > 35%).Aims
Methods
The primary aim of this study was to determine the rates of return to work (RTW) and sport (RTS) following a humeral shaft fracture. The secondary aim was to identify factors independently associated with failure to RTW or RTS. From 2008 to 2017, all patients with a humeral diaphyseal fracture were retrospectively identified. Patient demographics and injury characteristics were recorded. Details of pre-injury employment, sporting participation, and levels of return post-injury were obtained via postal questionnaire. The University of California, Los Angeles (UCLA) Activity Scale was used to quantify physical activity among active patients. Regression was used to determine factors independently associated with failure to RTW or RTS.Aims
Methods
