Aims. 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. Methods. 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
Pelvic tilt (PT) can significantly change the functional orientation of the acetabular component and may differ markedly between patients undergoing total hip arthroplasty (THA). Patients with stiff spines who have little change in PT are considered at high risk for instability following THA. Femoral component position also contributes to the limits of impingement-free range of motion (ROM), but has been less studied. Little is known about the impact of combined anteversion on risk of impingement with changing pelvic position. We used a virtual hip ROM (vROM) tool to investigate whether there is an ideal functional combined anteversion for reduced risk of hip impingement. We collected PT information from functional lateral radiographs (standing and sitting) and a supine CT scan, which was then input into the vROM tool. We developed a novel vROM scoring system, considering both seated flexion and standing extension manoeuvres, to quantify whether hips had limited ROM and then correlated the vROM score to component position.Aims
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