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
Aims. Conventional patient-reported surveys, used for patients undergoing total hip arthroplasty (THA), are limited by subjectivity and recall bias. Objective functional evaluation, such as
To systematically review the efficacy of split tendon transfer surgery on gait-related outcomes for children and adolescents with cerebral palsy (CP) and spastic equinovarus foot deformity. Five databases (CENTRAL, CINAHL, PubMed, Embase, Web of Science) were systematically screened for studies investigating split tibialis anterior or split tibialis posterior tendon transfer for spastic equinovarus foot deformity, with gait-related outcomes (published pre-September 2022). Study quality and evidence were assessed using the Methodological Index for Non-Randomized Studies, the Risk of Bias In Non-Randomized Studies of Interventions, and the Grading of Recommendations Assessment, Development and Evaluation.Aims
Methods
Biofilm-related infection is a major complication that occurs in orthopaedic surgery. Various treatments are available but efficacy to eradicate infections varies significantly. A systematic review was performed to evaluate therapeutic interventions combating biofilm-related infections on in vivo animal models. Literature research was performed on PubMed and Embase databases. Keywords used for search criteria were “bone AND biofilm”. Information on the species of the animal model, bacterial strain, evaluation of biofilm and bone infection, complications, key findings on observations, prevention, and treatment of biofilm were extracted.Aims
Methods
To analyze outcomes reported in studies of Ponseti correction of idiopathic clubfoot. A systematic review of the literature was performed to identify a list of outcomes and outcome tools reported in the literature. A total of 865 studies were screened following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and 124 trials were included in the analysis. Data extraction was completed by two researchers for each trial. Each outcome tool was assigned to one of the five core areas defined by the Outcome Measures Recommended for use in Randomized Clinical Trials (OMERACT). Bias assessment was not deemed necessary for the purpose of this paper.Aims
Methods
The success of anterior cruciate ligament reconstruction (ACLR)
depends on osseointegration at the graft-tunnel interface and intra-articular
ligamentization. Our aim was to conduct a systematic review of clinical
and preclinical studies that evaluated biological augmentation of
graft healing in ACLR. In all, 1879 studies were identified across three databases.
Following assessment against strict criteria, 112 studies were included
(20 clinical studies; 92 animal studies). Aims
Materials and Methods
