Post-traumatic periprosthetic acetabular fractures are rare but serious. Few studies carried out on small cohorts have reported them in the literature. The aim of this work is to describe the specific characteristics of post-traumatic periprosthetic acetabular fractures, and the outcome of their surgical treatment in terms of function and complications. Patients with this type of fracture were identified retrospectively over a period of six years (January 2016 to December 2021). The following data were collected: demographic characteristics, date of insertion of the prosthesis, details of the intervention, date of the trauma, characteristics of the fracture, and type of treatment. Functional results were assessed with the Harris Hip Score (HHS). Data concerning complications of treatment were collected.Aims
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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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