Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy.Aims
Methods
Periprosthetic hip-joint infection is a multifaceted and highly detrimental outcome for patients and clinicians. The incidence of prosthetic joint infection reported within two years of primary hip arthroplasty ranges from 0.8% to 2.1%. Costs of treatment are over five-times greater in people with periprosthetic hip joint infection than in those with no infection. Currently, there are no national evidence-based guidelines for treatment and management of this condition to guide clinical practice or to inform clinical study design. The aim of this study is to develop guidelines based on evidence from the six-year INFection and ORthopaedic Management (INFORM) research programme. We used a consensus process consisting of an evidence review to generate items for the guidelines and online consensus questionnaire and virtual face-to-face consensus meeting to draft the guidelines.Aims
Methods
To evaluate how abnormal proximal femoral anatomy affects different femoral version measurements in young patients with hip pain. First, femoral version was measured in 50 hips of symptomatic consecutively selected patients with hip pain (mean age 20 years (SD 6), 60% (n = 25) females) on preoperative CT scans using different measurement methods: Lee et al, Reikerås et al, Tomczak et al, and Murphy et al. Neck-shaft angle (NSA) and α angle were measured on coronal and radial CT images. Second, CT scans from three patients with femoral retroversion, normal femoral version, and anteversion were used to create 3D femur models, which were manipulated to generate models with different NSAs and different cam lesions, resulting in eight models per patient. Femoral version measurements were repeated on manipulated femora.Aims
Methods
