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
Posterior malleolar (PM) fractures are commonly associated with ankle fractures, pilon fractures, and to a lesser extent tibial shaft fractures. The tibialis posterior (TP) tendon entrapment is a rare complication associated with PM fractures. If undiagnosed, TP entrapment is associated with complications, ranging from reduced range of ankle movement to instability and pes planus deformities, which require further surgeries including radical treatments such as arthrodesis. The inclusion criteria applied in PubMed, Scopus, and Medline database searches were: all adult studies published between 2012 and 2022; and studies written in English. Outcome of TP entrapment in patients with ankle injuries was assessed by two reviewers independently.Aims
Methods
