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 five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. Conclusion. These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929–937

Aims

The aims of this study were to: 1) report on a cohort of skeletally mature patients with native hip and knee septic arthritis over a 14-year period; 2) to determine the rate of joint failure in patients who had experienced an episode of hip or knee septic arthritis; and 3) to assess the outcome following septic arthritis relative to the infecting organism, whether those patients infected by Staphylococcus aureus would be more likely to have adverse outcomes than those infected by other organisms.

A consecutive series of 30 total hip replacements using a hydroxyapatite (HA)-coated, modular implant (Omnifit) was followed clinically and by roentgen stereophotogrammetric analysis for two years and compared with two control groups, one of 27 cemented Charnley sockets and one of 40 cemented Charnley stems. Omnifit sockets with a central gap between the dome of the socket and the acetabular bone in the postoperative radiographs, migrated less than sockets without such gaps (p = 0.01). After adjustment for patient-related factors (age, gender and weight), no significant difference was found between the two prostheses with respect to micromotion and wear. We conclude that the early fixation of the HA-coated Omnifit prosthesis compares with that of the cemented Charnley prosthesis