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 surgical helmet system (SHS) was developed to reduce the risk of periprosthetic joint infection (PJI), but the evidence is contradictory, with some studies suggesting an increased risk of PJI due to potential leakage through the glove-gown interface (GGI) caused by its positive pressure. We assumed that SHS and glove exchange had an impact on the leakage via GGI. Methods. There were 404 arthroplasty simulations with fluorescent gel, in which SHS was used (H+) or not (H-), and GGI was sealed (S+) or not (S-), divided into four groups: H+S+, H+S-, H-S+, and H-S-, varying by exposure duration (15 to 60 minutes) and frequency of glove exchanges (0 to 6 times). The intensity of fluorescent leakage through GGI was quantified automatically with an image analysis software. The effect of the above factors on fluorescent leakage via GGI were compared and analyzed. Results. The leakage intensity increased with exposure duration and frequency of glove exchanges in all groups. When SHS was used and GGI was not sealed (H+S-), the leakage intensity via GGI had the fastest increase, consistently higher than other groups (H+S+, H-S+ and H-S-) after 30 minutes (p < 0.05) and when there were more than four instances of glove exchange (p < 0.05). Additionally, the leakage was strongly correlated with the duration of exposure (r. s. = 0.8379; p < 0.050) and the frequency of glove exchange (r. s. = 0.8198; p < 0.050) in H+S-. The correlations with duration and frequency turned weak when SHS was not used (H-) or GGI was sealed off (S+). Conclusion. Due to personal protection, SHS is recommended in arthroplasties. Meanwhile, it is strongly recommended to seal the GGI of the inner gloves and exchange the outer gloves hourly to reduce the risk of contamination from SHS. Cite this article: Bone Jt Open 2023;4(11):859–864

Aims

The aim of this study was to conduct the largest low contact stress (LCS) retrieval study to elucidate the failure mechanisms of the Porocoat and Duofix femoral component. The latter design was voluntarily recalled by the manufacturer.

Digital radiography is becoming widespread. Accurate pre-operative templating of digital images of the hip traditionally involves positioning a calibration object at its centre. This can be difficult and cause embarrassment. We have devised a method whereby a planar disc placed on the radiographic cassette accounts for the expected magnification. Initial examination of 50 pelvic CT scans showed a mean hip centre distance of 117 mm (79 to 142) above the gluteal skin. Further calculations predicted that a disc of 37.17 mm diameter, placed on the cassette, would appear identical to a 30 mm sphere placed at the level of the centre of the hip as requested by our templating software. We assessed accuracy and reproducibility by ‘reverse calibration’ of 20 radiographs taken three months after hip replacement using simultaneous sphere and disc methods, and a further 20 with a precision disc of accurate size. Even when variations in patient size were ignored, the disc proved more accurate and reliable than the sphere.

The technique is reliable, robust, cost effective and acceptable to patients and radiographers. It can easily be used in any radiography department after a few simple calculations and manufacture of appropriately-sized discs.