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. 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.Aims
Methods
A new generation of knee prostheses has been introduced with the intention of improving post-operative knee flexion. In order to evaluate whether this goal has been achieved we performed a systematic review and meta-analysis. Systematic literature searches were conducted on MEDLINE and EMBASE from their inception to December 2007, and proceedings of scientific meetings were also searched. Only randomised, clinical trials were included in the meta-analysis. The mean difference in the maximum post-operative flexion between the ‘high-flex’ and conventional types of prosthesis was defined as the primary outcome measure. A total of five relevant articles was identified. Analysis of these trials suggested that no clinically relevant or statistically significant improvement was obtained in flexion with the ‘high-flex’ prostheses. The weighted mean difference was 2.1° (95% confidence interval −0.2 to +4.3; p = 0.07).
