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
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Magnetically controlled growing rods (MCGR) have been gaining popularity in the management of early-onset scoliosis (EOS) over the past decade. We present our experience with the first 44 MCGR consecutive cases treated at our institution. This is a retrospective review of consecutive cases of MCGR performed in our institution between 2012 and 2018. This cohort consisted of 44 children (25 females and 19 males), with a mean age of 7.9 years (3.7 to 13.6). There were 41 primary cases and three revisions from other rod systems. The majority (38 children) had dual rods. The group represents a mixed aetiology including idiopathic (20), neuromuscular (13), syndromic (9), and congenital (2). The mean follow-up was 4.1 years, with a minimum of two years. Nine children graduated to definitive fusion. We evaluated radiological parameters of deformity correction (Cobb angle), and spinal growth (T1-T12 and T1-S1 heights), as well as complications during the course of treatment.Aims
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