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
This systematic review and meta-analysis was conducted to compare open reduction and internal fixation (ORIF) with primary arthrodesis (PA) in the treatment of Lisfranc injuries, regarding patient-reported outcome measures (PROMs), and risk of secondary surgery. The aim was to conclusively determine the best available treatment based on the most complete and recent evidence available. A systematic search was conducted in PubMed, Cochrane Controlled Register of Trials (CENTRAL), EMBASE, CINAHL, PEDro, and SPORTDiscus. Additionally, ongoing trial registers and reference lists of included articles were screened. Risk of bias (RoB) and level of evidence were assessed using the Cochrane risk of bias tools and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool. The random and fixed-effect models were used for the statistical analysis.Aims
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