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

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The early diagnosis of cauda equina syndrome (CES) is crucial for a favourable outcome. Several studies have reported the use of an ultrasound scan of the bladder as an adjunct to assess the minimum post-void residual volume of urine (mPVR). However, variable mPVR values have been proposed as a threshold without consensus on a value for predicting CES among patients with relevant symptoms and signs. The aim of this study was to perform a meta-analysis and systematic review of the published evidence to identify a threshold mPVR value which would provide the highest diagnostic accuracy in patients in whom the diagnosis of CES is suspected.

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This systematic review aimed to summarize the full range of complications reported following ankle arthroscopy and the frequency at which they occur.

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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Sarcopenia is characterized by a generalized progressive loss of skeletal muscle mass, strength, and physical performance. This systematic review primarily evaluated the effects of sarcopenia on postoperative functional recovery and mortality in patients undergoing orthopaedic surgery, and secondarily assessed the methods used to diagnose and define sarcopenia in the orthopaedic literature.

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Cervical radiculopathy is a significant cause of pain and morbidity. For patients with severe and poorly controlled symptoms who may not be candidates for surgical management, treatment with transforaminal epidural steroid injections (CTFESI) has gained widespread acceptance. However, a paucity of high-quality evidence supporting their use balanced against perceived high risks of the procedure potentially undermines the confidence of clinicians who use the technique. We undertook a systematic review of the available literature regarding CTFESI to assess the clinical efficacy and complication rates of the procedure.

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Current guidelines recommend surgery within 48 hours among patients presenting with hip fractures; however, optimal surgical timing for patients on oral anticoagulants (OACs) remains unclear. Individual studies are limited by small sample sizes and heterogeneous outcomes. The aim of this study was to conduct a systematic review and meta-analysis to summarize the effect of pre-injury OACs on time-to-surgery (TTS) and all-cause mortality among older adults with hip fracture treated surgically.

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Computer-based applications are increasingly being used by orthopaedic surgeons in their clinical practice. With the integration of technology in surgery, augmented reality (AR) may become an important tool for surgeons in the future. By superimposing a digital image on a user’s view of the physical world, this technology shows great promise in orthopaedics. The aim of this review is to investigate the current and potential uses of AR in orthopaedics.

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The aims of this systematic review were to describe the quantity and methodological quality of meta-analyses in orthopaedic surgery published during the last 17 years.

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Dupuytren’s contracture is a benign, myoproliferative condition affecting the palmar fascia that results in progressive contractures of the fingers. Despite increased knowledge of the cellular and connective tissue changes involved, neither a cure nor an optimum form of treatment exists. The aim of this systematic review was to summarize the best available evidence on the management of this condition.