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
Prolonged waits for hip and knee arthroplasty have raised questions about the equity of current approaches to waiting list prioritization for those awaiting surgery. We therefore set out to understand key stakeholder (patient and surgeon) preferences for the prioritization of patients awaiting such surgery, in order to guide future waiting list redesign. A combined qualitative/quantitative approach was used. This comprised a Delphi study to first inform which factors patients and surgeons designate as important for prioritization of patients on hip and knee arthroplasty waiting lists, followed by a discrete choice experiment (DCE) to determine how the factors should be weighed against each other. Coefficient values for each included DCE attribute were used to construct a ‘priority score’ (weighted benefit score) that could be used to rank individual patients waiting for surgery based on their respective characteristics.Aims
Methods
The aim of this study was to gain a consensus for best practice of the assessment and management of children with idiopathic toe walking (ITW) in order to provide a benchmark for practitioners and guide the best consistent care. An established Delphi approach with predetermined steps and degree of agreement based on a standardized protocol was used to determine consensus. The steering group members and Delphi survey participants included members from the British Society of Children’s Orthopaedic Surgery (BSCOS) and the Association of Paediatric Chartered Physiotherapists (APCP). The statements included definition, assessment, treatment indications, nonoperative and operative interventions, and outcomes. Descriptive statistics were used for analysis of the Delphi survey results. The AGREE checklist was followed for reporting the results.Aims
Methods
Surgeon and patient reluctance to participate are potential significant barriers to conducting placebo-controlled trials of orthopaedic surgery. Understanding the preferences of orthopaedic surgeons and patients regarding the design of randomized placebo-controlled trials (RCT-Ps) of knee procedures can help to identify what RCT-P features will lead to the greatest participation. This information could inform future trial designs and feasibility assessments. This study used two discrete choice experiments (DCEs) to determine which features of RCT-Ps of knee procedures influence surgeon and patient participation. A mixed-methods approach informed the DCE development. The DCEs were analyzed with a baseline category multinomial logit model.Aims
Methods
The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population. We used an active appearance model-based approach to segment the preoperative CT of 21 osteoarthritic patients, which identified the osteophyte-free surfaces and estimated cartilage from the segmented bones; these geometries were used to construct patient-specific musculoskeletal models of the pre-diseased knee. Subsequently, implantations were simulated using the MA method, and a previously developed optimization technique was employed to find the optimal implant position that minimized the root mean square deviation between pre-diseased and postoperative ligament strains and kinematics.Aims
Methods
High-quality clinical research in children’s orthopaedic surgery
has lagged behind other surgical subspecialties. This study used
a consensus-based approach to identify research priorities for clinical
trials in children’s orthopaedics. A modified Delphi technique was used, which involved an initial
scoping survey, a two-round Delphi process and an expert panel formed
of members of the British Society of Children’s Orthopaedic Surgery.
The survey was conducted amongst orthopaedic surgeons treating children
in the United Kingdom and Ireland.Aims
Methods
