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. 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. Methods. This systematic review and network meta-analysis used a Bayesian random-effects model to evaluate the direct and indirect comparisons among all treatment options. PubMed, Web of Science, Clinicaltrial.gov, EMBASE, MEDLINE, and the Cochrane Library were searched from inception to February 2023. Randomized controlled trials (RCTs) which evaluate the efficacy of HA, PRP, local anaesthetic, steroid, steroid+anaesthetic, HA+PRP, and physiological saline injection as a placebo, for patients with hip OA were included. Results. In this meta-analysis of 16 RCTs with a total of 1,735 participants, steroid injection was found to be significantly more effective than placebo injection on reported pain at three months, but no significant difference was observed at six months. Furthermore, steroid injection was considerably more effective than placebo injection for functional outcomes at three months, while the combination of HA+PRP injection was substantially more effective at six months. Conclusion. Evidence suggests that steroid injection is more effective than saline injection for the treatment of hip joint pain, and restoration of functional outcomes. Cite this article: Bone Joint J 2024;106-B(6):532–539

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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.

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 answer two questions: what are the best diagnostic methods for diagnosing bacterial arthritis of a native joint?; and what are the most commonly used definitions for bacterial arthritis of a native joint?

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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?

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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.

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Dislocation is the most common indication for further surgery following total hip arthroplasty (THA) when undertaken in patients with a femoral neck fracture. This study aimed to assess the complication rates of THA with dual mobility components (THA-DMC) following a femoral neck fracture and to compare outcomes between THA-DMC, conventional THA, and hemiarthroplasty (HA).

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We performed a meta-analysis investigating the association between preoperative psychological distress and postoperative pain and function after total knee arthroplasty (TKA).