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
We present the development of a day-case total hip arthroplasty (THA) pathway in a UK National Health Service institution in conjunction with an extensive evidence-based summary of the interventions used to achieve successful day-case THA to which the protocol is founded upon. We performed a prospective audit of day-case THA in our institution as we reinitiate our full capacity elective services. In parallel, we performed a review of the literature reporting complication or readmission rates at ≥ 30-day postoperative following day-case THA. Electronic searches were performed using four databases from the date of inception to November 2020. Relevant studies were identified, data extracted, and qualitative synthesis performed.Aims
Methods
