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
We compared the clinical outcomes of curved intertrochanteric varus osteotomy (CVO) with bone impaction grafting (BIG) with CVO alone for the treatment of osteonecrosis of the femoral head (ONFH). This retrospective comparative study included 81 patients with ONFH; 37 patients (40 hips) underwent CVO with BIG (BIG group) and 44 patients (47 hips) underwent CVO alone (CVO group). Patients in the BIG group were followed-up for a mean of 12.2 years (10.0 to 16.5). Patients in the CVO group were followed-up for a mean of 14.5 years (10.0 to 21.0). Assessment parameters included the Harris Hip Score (HHS), Oxford Hip Score (OHS), Japanese Orthopaedic Association Hip-Disease Evaluation Questionnaire (JHEQ), complication rates, and survival rates, with conversion to total hip arthroplasty (THA) and radiological failure as the endpoints.Aims
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