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
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Accumulated evidence indicates that local cell origins may ingrain differences in the phenotypic activity of human osteoblasts. We hypothesized that these differences may also exist in osteoblasts harvested from the same bone type at periarticular sites, including those adjacent to the fixation sites for total joint implant components. Human osteoblasts were obtained from the acetabulum and femoral neck of seven patients undergoing total hip arthroplasty (THA) and from the femoral and tibial cuts of six patients undergoing total knee arthroplasty (TKA). Osteoblasts were extracted from the usually discarded bone via enzyme digestion, characterized by flow cytometry, and cultured to passage three before measurement of metabolic activity, collagen production, alkaline phosphatase (ALP) expression, and mineralization.Aims
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