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 prevalence of ipsilateral total hip arthroplasty (THA) and total knee arthroplasty (TKA) is rising in concert with life expectancy, putting more patients at risk for interprosthetic femur fractures (IPFFs). Our study aimed to assess treatment methodologies, implant survivorship, and IPFF clinical outcomes. A total of 76 patients treated for an IPFF from February 1985 to April 2018 were reviewed. Prior to fracture, at the hip/knee sites respectively, 46 femora had primary/primary, 21 had revision/primary, three had primary/revision, and six had revision/revision components. Mean age and BMI were 74 years (33 to 99) and 30 kg/m2 (21 to 46), respectively. Mean follow-up after fracture treatment was seven years (2 to 24).Aims
Methods
