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
The goal of the current systematic review was to assess the impact of implant placement accuracy on outcomes following total knee arthroplasty (TKA). A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines using the Ovid Medline, Embase, Cochrane Central, and Web of Science databases in order to assess the impact of the patient-reported outcomes measures (PROMs) and implant placement accuracy on outcomes following TKA. Studies assessing the impact of implant alignment, rotation, size, overhang, or condylar offset were included. Study quality was assessed, evidence was graded (one-star: no evidence, two-star: limited evidence, three-star: moderate evidence, four-star: strong evidence), and recommendations were made based on the available evidence.Aims
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