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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Return to sport following undergoing total (TKA) and unicompartmental knee arthroplasty (UKA) has been researched with meta-analyses and systematic reviews of varying quality. The aim of this study is to create an umbrella review to consolidate the data into consensus guidelines for returning to sports following TKA and UKA.

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The aims of this study were to identify and evaluate the current literature examining the prognostic factors which are associated with failure of total elbow arthroplasty (TEA).

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The aim of this study was to determine the effectiveness of home-based prehabilitation on pre- and postoperative outcomes in participants awaiting total knee (TKA) and hip arthroplasty (THA).

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Patients with metabolic syndrome (MetS) are known to be at increased risk of postoperative complications, but it is unclear whether MetS is also associated with complications after total hip arthroplasty (THA) or total knee arthroplasty (TKA). Here, we perform a systematic review and meta-analysis linking MetS to postoperative complications in THA and TKA.

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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 aims of this systematic review were to assess the learning curve of semi-active robotic arm-assisted total hip arthroplasty (rTHA), and to compare the accuracy, patient-reported functional outcomes, complications, and survivorship between rTHA and manual total hip arthroplasty (mTHA).

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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).

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There is inconsistent evidence on whether prior spinal fusion surgery adversely impacts outcomes following total hip arthroplasty (THA). We conducted a systematic review and meta-analysis to assess the association between pre-existing spinal fusion surgery and the rate of complications following primary THA.

Aims. Arthroplasty is being increasingly used for the management of distal humeral fractures (DHFs) in elderly patients. Arthroplasty options include total elbow arthroplasty (TEA) and hemiarthroplasty (HA); both have unique complications and there is not yet a consensus on which implant is superior. This systematic review asked: in patients aged over 65 years with unreconstructable DHFs, what differences are there in outcomes, as measured by patient-reported outcome measures (PROMs), range of motion (ROM), and complications, between distal humeral HA and TEA?. Methods. A systematic review of the literature was performed via a search of MEDLINE and Embase. Two reviewers extracted data on PROMs, ROM, and complications. PROMs and ROM results were reported descriptively and a meta-analysis of complications was conducted. Quality of methodology was assessed using Wylde’s non-summative four-point system. The study was registered with PROSPERO (CRD42021228329). Results. A total of 29 studies met the inclusion and exclusion criteria. The mean Disabilities of the Arm, Shoulder and Hand questionnaire (DASH) score was 19.6 (SD 7.5) for HA and 38 (SD 11.9) for TEA and the mean abbreviated version of DASH was 17.2 (SD 13.2) for HA and 24.9 (SD 4.8) for TEA. The Mayo Elbow Performance Score was the most commonly reported PROM across included studies, with a mean of 87 (SD 5.3) in HA and 88.3 (SD 5) in TEA. High complication rates were seen in both HA (22% (95% confidence interval (CI) 5 to 44)) and TEA (21% (95% CI 13 to 30), but no statistically significant difference identified. Conclusion. This systematic review has indicated PROMs and ROM mostly favouring HA, but with a similarly high complication rate in the two procedures. However, due to the small sample size and heterogeneity between studies, strength of evidence for these findings is low. We propose further research in the form of a national randomized controlled trial. Cite this article: Bone Joint J 2022;104-B(5):559–566

Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Aims. The aim of this study was to investigate the outcome of periprosthetic fractures of the humerus and to assess the uniformity of the classifications used for these fractures (including those around elbow and/or shoulder arthroplasties) by performing a systematic review of the literature. Methods. A systematic search was conducted using the National Institute for Health and Care Excellence Healthcare Databases Advance Search. For inclusion, studies had to report clinical outcomes following the management of periprosthetic fractures of the humerus. The protocol was registered on the PROSPERO database. Results. Overall, 40 studies were included, involving a total of 210 patients. The fractures were reported using very heterogeneous classification systems, as were the functional outcome scores. A total of 60 patients had nonoperative treatment with a 50% rate of nonunion. Fixation was undertaken in 99 patients; successful union was obtained in 93 (93%). Revision of either the humeral stem or the whole arthroplasty was reported in 79 patients with a high rate of union (n = 66; 84%), and a mean rate of complications of 29% (0% to 41%). Conclusion. This study highlighted a lack of uniformity in classifying these fractures and reporting the outcome of their treatment. The results may help to inform decision-making with patients, particularly about the rate of complications of nonoperative treatment. There is a need to improve the reporting of the pattern of these fractures using a uniform classification system, and the harmonization of the collection of data relating to the outcome of treatment. Based on this review, we propose a minimum dataset to be used in future studies. Cite this article: Bone Joint J 2022;104-B(4):416–423

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Proximal humeral fractures are the third most common fracture among the elderly. Complications associated with fixation include screw perforation, varus collapse, and avascular necrosis of the humeral head. To address these challenges, various augmentation techniques to increase medial column support have been developed. There are currently no recent studies that definitively establish the superiority of augmented fixation over non-augmented implants in the surgical treatment of proximal humeral fractures. The aim of this systematic review and meta-analysis was to compare the outcomes of patients who underwent locking-plate fixation with cement augmentation or bone-graft augmentation versus those who underwent locking-plate fixation without augmentation for proximal humeral fractures.

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In the UK, the agricultural, military, and construction sectors have stringent rules about the use of hearing protection due to the risk of noise-induced hearing loss. Orthopaedic staff may also be at risk due to the use of power tools. The UK Health and Safety Executive (HSE) have clear standards as to what are deemed acceptable occupational levels of noise on A-weighted and C-weighted scales. The aims of this review were to assess the current evidence on the testing of exposure to noise in orthopaedic operating theatres to see if it exceeds these regulations.

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Robotic-assisted unicompartmental knee arthroplasty (R-UKA) has been proposed as an approach to improve the results of the conventional manual UKA (C-UKA). The aim of this meta-analysis was to analyze the studies comparing R-UKA and C-UKA in terms of clinical outcomes, radiological results, operating time, complications, and revisions.

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Intra-articular (IA) injection may be used when treating hip osteoarthritis (OA). Common injections include steroids, hyaluronic acid (HA), local anaesthetic, and platelet-rich plasma (PRP). Network meta-analysis allows for comparisons between two or more treatment groups and uses direct and indirect comparisons between interventions. This network meta-analysis aims to compare the efficacy of various IA injections used in the management of hip OA with a follow-up of up to six months.

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As an increasing number of female surgeons are choosing orthopaedics, it is important to recognize the impact of pregnancy within this cohort. The aim of this review was to examine common themes and data surrounding pregnancy, parenthood, and fertility within orthopaedics.

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The aim of this study was to perform a systematic review of the evidence for the use of intraoperative cell salvage in patients undergoing revision hip arthroplasty, and specifically to analyze the available data in order to quantify any associated reduction in the use of allogenic blood transfusion, and the volume which is used.

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The aim of this study was to report the pooled prevalence of post-traumatic osteoarthritis (PTOA) and examine whether the risk of developing PTOA after anterior cruciate ligament (ACL) injury has decreased in recent decades.