Aims. The preoperative diagnosis of periprosthetic joint infection (PJI) remains a challenge due to a lack of
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 aim of this review was to evaluate the available literature
and to calculate the pooled sensitivity and specificity for the
different alpha-defensin test systems that may be used to diagnose
prosthetic joint infection (PJI). Studies using alpha-defensin or Synovasure (Zimmer Biomet, Warsaw,
Indiana) to diagnose PJI were identified from systematic searches
of electronic databases. The quality of the studies was evaluated
using the Quality Assessment of Studies of Diagnostic Accuracy (QUADAS)
tool. Meta-analysis was completed using a bivariate model.Aims
Materials and Methods