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 follow-up interval of a study represents an important aspect that is frequently mentioned in the title of the manuscript. Authors arbitrarily define whether the follow-up of their study is short-, mid-, or long-term. There is no clear consensus in that regard and definitions show a large range of variation. It was therefore the aim of this study to systematically identify clinical research published in high-impact orthopaedic journals in the last five years and extract follow-up information to deduce corresponding evidence-based definitions of short-, mid-, and long-term follow-up. A systematic literature search was performed to identify papers published in the six highest ranked orthopaedic journals during the years 2015 to 2019. Follow-up intervals were analyzed. Each article was assigned to a corresponding subspecialty field: sports traumatology, knee arthroplasty and reconstruction, hip-preserving surgery, hip arthroplasty, shoulder and elbow arthroplasty, hand and wrist, foot and ankle, paediatric orthopaedics, orthopaedic trauma, spine, and tumour. Mean follow-up data were tabulated for the corresponding subspecialty fields. Comparison between means was conducted using analysis of variance.Aims
Methods
