Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article: Bone Joint J 2022;104-B(12):1292–1303

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This study aimed to compare the effect of antibiotic-loaded bone cement (ALBC) versus plain bone cement (PBC) on revision rates for periprosthetic joint infection (PJI) and all-cause revisions following primary elective total hip arthroplasty (THA) and total knee arthroplasty (TKA).

The application of robotics in the operating theatre for knee arthroplasty remains controversial. As with all new technology, the introduction of new systems might be associated with a learning curve. However, guidelines on how to assess the introduction of robotics in the operating theatre are lacking. This systematic review aims to evaluate the current evidence on the learning curve of robot-assisted knee arthroplasty. An extensive literature search of PubMed, Medline, Embase, Web of Science, and Cochrane Library was conducted. Randomized controlled trials, comparative studies, and cohort studies were included. Outcomes assessed included: time required for surgery, stress levels of the surgical team, complications in regard to surgical experience level or time needed for surgery, size prediction of preoperative templating, and alignment according to the number of knee arthroplasties performed. A total of 11 studies met the inclusion criteria. Most were of medium to low quality. The operating time of robot-assisted total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA) is associated with a learning curve of between six to 20 cases and six to 36 cases respectively. Surgical team stress levels show a learning curve of seven cases in TKA and six cases for UKA. Experience with the robotic systems did not influence implant positioning, preoperative planning, and postoperative complications. Robot-assisted TKA and UKA is associated with a learning curve regarding operating time and surgical team stress levels. Future evaluation of robotics in the operating theatre should include detailed measurement of the various aspects of the total operating time, including total robotic time and time needed for preoperative planning. The prior experience of the surgical team should also be evaluated and reported. Cite this article: Bone Joint J 2020;102-B(4):407–413

Aims. The aims of this study were to validate the outcome of total elbow arthroplasty (TEA) in patients with rheumatoid arthritis (RA), and to identify factors that affect the outcome. Methods. We searched PubMed, MEDLINE, Cochrane Reviews, and Embase from between January 2003 and March 2019. The primary aim was to determine the implant failure rate, the mode of failure, and risk factors predisposing to failure. A secondary aim was to identify the overall complication rate, associated risk factors, and clinical performance. A meta-regression analysis was completed to identify the association between each parameter with the outcome. Results. A total of 38 studies including 2,118 TEAs were included in the study. The mean follow-up was 80.9 months (8.2 to 156). The implant failure and complication rates were 16.1% (95% confidence interval (CI) 0.128 to 0.200) and 24.5% (95% CI 0.203 to 0.293), respectively. Aseptic loosening was the most common mode of failure (9.5%; 95% CI 0.071 to 0.124). The mean postoperative ranges of motion (ROMs) were: flexion 131.5° (124.2° to 138.8°), extension 29.3° (26.8° to 31.9°), pronation 74.0° (67.8° to 80.2°), and supination 72.5° (69.5° to 75.5°), and the mean postoperative Mayo Elbow Performance Score (MEPS) was 89.3 (95% CI 86.9 to 91.6). The meta-regression analysis identified that younger patients and implants with an unlinked design correlated with higher failure rates. Younger patients were associated with increased complications, while female patients and an unlinked prosthesis were associated with aseptic loosening. Conclusion. TEA continues to provide satisfactory results for patients with RA. However, it is associated with a substantially higher implant failure and complication rates compared with hip and knee arthroplasties. The patient’s age, sex, and whether cemented fixation and unlinked prosthesis were used can influence the outcome. Level of Evidence: Therapeutic Level IV. Cite this article: Bone Joint J 2020;102-B(8):967–980

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This systematic review aims to compare the precision of component positioning, patient-reported outcome measures (PROMs), complications, survivorship, cost-effectiveness, and learning curves of MAKO robotic arm-assisted unicompartmental knee arthroplasty (RAUKA) with manual medial unicompartmental knee arthroplasty (mUKA).

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The preoperative diagnosis of periprosthetic joint infection (PJI) remains a challenge due to a lack of biomarkers that are both sensitive and specific. We investigated the performance characteristics of polymerase chain reaction (PCR), interleukin-6 (IL6), and calprotectin of synovial fluid in the diagnosis of PJI.

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The goal of the current systematic review was to assess the impact of implant placement accuracy on outcomes following total knee arthroplasty (TKA).

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The aim of this review was to evaluate the available literature and to calculate the pooled sensitivity and specificity for the different alpha-defensin test systems that may be used to diagnose prosthetic joint infection (PJI).

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Periprosthetic joint infection (PJI) is a serious complication of total hip arthroplasty (THA). Different bearing surface materials have different surface properties and it has been suggested that the choice of bearing surface may influence the risk of PJI after THA. The objective of this meta-analysis was to compare the rate of PJI between metal-on-polyethylene (MoP), ceramic-on-polyethylene (CoP), and ceramic-on-ceramic (CoC) bearings.