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 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. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680

Aims. Femoroacetabular Junction Impingement (FAI) describes abnormalities in the shape of the femoral head–neck junction, or abnormalities in the orientation of the acetabulum. In the short term, FAI can give rise to pain and disability, and in the long-term it significantly increases the risk of developing osteoarthritis. The Femoroacetabular Impingement Trial (FAIT) aims to determine whether operative or non-operative intervention is more effective at improving symptoms and preventing the development and progression of osteoarthritis. . Methods. FAIT is a multicentre superiority parallel two-arm randomised controlled trial comparing physiotherapy and activity modification with arthroscopic surgery for the treatment of symptomatic FAI. Patients aged 18 to 60 with clinical and radiological evidence of FAI are eligible. Principal exclusion criteria include previous surgery to the index hip, established osteoarthritis (Kellgren–Lawrence ≥ 2), hip dysplasia (centre-edge angle < 20°), and completion of a physiotherapy programme targeting FAI within the previous 12 months. Recruitment will take place over 24 months and 120 patients will be randomised in a 1:1 ratio and followed up for three years. The two primary outcome measures are change in hip outcome score eight months post-randomisation (approximately six-months post-intervention initiation) and change in radiographic minimum joint space width 38 months post-randomisation. ClinicalTrials.gov: NCT01893034. Cite this article: Bone Joint Res 2014;3:321–7

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Computer-assisted 3D preoperative planning software has the potential to improve postoperative stability in total hip arthroplasty (THA). Commonly, preoperative protocols simulate two functional positions (standing and relaxed sitting) but do not consider other common positions that may increase postoperative impingement and possible dislocation. This study investigates the feasibility of simulating commonly encountered positions, and positions with an increased risk of impingement, to lower postoperative impingement risk in a CT-based 3D model.

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Iliopsoas impingement occurs in 4% to 30% of patients after undergoing total hip arthroplasty (THA). Despite a relatively high incidence, there are few attempts at modelling impingement between the iliopsoas and acetabular component, and no attempts at modelling this in a representative cohort of subjects. The purpose of this study was to develop a novel computational model for quantifying the impingement between the iliopsoas and acetabular component and validate its utility in a case-controlled investigation.

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The aim of this study was to identify the optimal lip position for total hip arthroplasties (THAs) using a lipped liner. There is a lack of consensus on the optimal position, with substantial variability in surgeon practice.

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Femoroacetabular impingement (FAI) patients report exacerbation of hip pain in deep flexion. However, the exact impingement location in deep flexion is unknown. The aim was to investigate impingement-free maximal flexion, impingement location, and if cam deformity causes hip impingement in flexion in FAI patients.

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Cam and pincer morphologies are potential precursors to hip osteoarthritis and important contributors to non-arthritic hip pain. However, only some hips with these pathomorphologies develop symptoms and joint degeneration, and it is not clear why. Anterior impingement between the femoral head-neck contour and acetabular rim in positions of hip flexion combined with rotation is a proposed pathomechanism in these hips, but this has not been studied in active postures. Our aim was to assess the anterior impingement pathomechanism in both active and passive postures with high hip flexion that are thought to provoke impingement.