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
Trauma and orthopaedics is the largest of the
surgical specialties and yet attracts a disproportionately small
fraction of available national and international funding for health
research. With the burden of musculoskeletal disease increasing,
high-quality research is required to improve the evidence base for
orthopaedic practice. Using the current research landscape in the
United Kingdom as an example, but also addressing the international
perspective, we highlight the issues surrounding poor levels of
research funding in trauma and orthopaedics and indicate avenues
for improving the impact and success of surgical musculoskeletal
research. Cite this article:
