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. 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.Aims
Methods
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:
The amount of glenoid bone loss is an important factor in deciding between soft-tissue and bony reconstruction when managing anterior shoulder instability. Accurate and reproducible measurement of glenoid bone loss is therefore vital in evaluation of shoulder instability and recommending specific treatment. The aim of this systematic review is to identify the range methods and measurement techniques employed in clinical studies treating glenoid bone loss. A systematic review of the PubMed, MEDLINE, and Embase databases was undertaken to cover a ten-year period from February 2011 to February 2021. We identified clinical studies that incorporated bone loss assessment in the methodology as part of the decision-making in the management of patients with anterior shoulder instability. The Preferred Reporting Items for Systematic Reviews (PRISMA) were used.Aims
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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). Searches of PubMed, Medline, and Google Scholar were performed in April 2020 in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Search terms included “robotic”, “hip”, and “arthroplasty”. The criteria for inclusion were published clinical research articles reporting the learning curve for rTHA (robotic arm-assisted only) and those comparing the implantation accuracy, functional outcomes, survivorship, or complications with mTHA.Aims
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This study evaluates the quality of patient-reported outcome measures (PROMs) reported in childhood fracture trials and recommends outcome measures to assess and report physical function, functional capacity, and quality of life using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) standards. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic review of OVID Medline, Embase, and Cochrane CENTRAL was performed to identify all PROMs reported in trials. A search of OVID Medline, Embase, and PsycINFO was performed to identify all PROMs with validation studies in childhood fractures. Development studies were identified through hand-searching. Data extraction was undertaken by two reviewers. Study quality and risk of bias was evaluated by COSMIN guidelines and recorded on standardized checklists.Aims
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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. A systematic review of the PubMed, MEDLINE, and Embase databases up to January 2019 using the keywords ‘orthopaedic’ OR ‘orthopedic AND augmented reality’ was performed by two independent reviewers.Aims
Materials and Methods
Acromial fractures following reverse shoulder arthroplasty (RSA) have a wide range of incidences in reported case series. This study evaluates their incidence following RSA by systematically reviewing the current literature. A systematic review using the search terms “reverse shoulder”, “reverse total shoulder”, or “inverted shoulder” was performed using PubMed, Web of Science, and Cochrane databases between 1 January 2010 and 31 March 2018. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used. Studies were included if they reported on RSA outcomes and the incidence rate of acromial and/or scapular spine fractures. The rate of these fractures was evaluated for primary RSA, revision RSA, RSA indications, and RSA implant design.Aims
Materials and Methods
