Aims. Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. Methods. The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. Results. Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after
To determine the trajectories of patient reported pain and functional disability over five years following total hip arthroplasty (THA) or total knee arthroplasty (TKA). A prospective, longitudinal cohort sub-study within the National Joint Registry (NJR) was undertaken. In all, 20,089 patients who underwent primary THA and 22,489 who underwent primary TKA between 2009 and 2010 were sent Oxford Hip Score (OHS) and Oxford Knee Score (OKS) questionnaires at six months, and one, three, and five years postoperatively. OHS and OKS were disaggregated into pain and function subscales. A k-means clustering procedure assigned each patient to a longitudinal trajectory group for pain and function. Ordinal regression was used to predict trajectory group membership using baseline OHS and OKS score, age, BMI, index of multiple deprivation, sex, ethnicity, geographical location, and American Society of Anesthesiologists grade.Aims
Methods
Aims. The primary aim of this study was to compare the wear properties of vitamin E-diffused, highly crosslinked polyethylene (VEPE) and one formulation of moderately crosslinked and mechanically annealed ultra-high molecular weight polyethylene (ModXLPE) in patients five years after primary total hip arthroplasty (THA). The secondary aim was to assess the clinical results of patients treated with VEPE by evaluating patient-reported outcome measures (PROMs), radiological evidence of fixation, and the incidence of mechanical failure. Patients and Methods. A total of 208 patients (221 THAs) from four international centres were recruited into a prospective study involving radiostereometric analysis (RSA) and the assessment of clinical outcomes. A total of 193 hips (87%) were reviewed at the