INTRODUCTION

Standing spinal alignment has been the center of focus recently, particularly in the setting of adult spinal deformity. Humans spend approximately half of their waking life in a seated position. While lumbopelvic sagittal alignment has been shown to adapt from standing to sitting posture, segmental vertebral alignment of the entire spine is not yet fully understood, nor are the effects of DEGEN or DEFORMITY. Segmental spinal alignment between sitting and standing, and the effects of degeneration and deformity were analyzed.

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

Abstract. Introduction. Recent reports show that spinopelvic mobility influences outcome following total hip arthroplasty. This scoping review investigates the relationship between spinopelvic parameters (SPPs) and symptomatic femoroacetabular impingement (FAI). Methods. A systematic search of EMBASE, PubMed and Cochrane for literature related to SPPs and FAI was undertaken as per PRISMA guidelines. Clinical outcome studies and prospective/retrospective studies investigating the role of SPPs in symptomatic FAI were included. Review articles, case reports and book chapters were excluded. Information extracted pertained to symptomatic cam deformities, pelvic tilt, acetabular version, biomechanics of dynamic movements and radiological FAI signs. Results. The search identified 42 papers for final analysis out of 1168 articles investigating the link between SPPs and pathological processes characteristic of FAI. Only one (2.4%) study was of level 1 evidence, five (11.9%) studies) were level 2, 17 (40.5%) were level 3 and 19 (45.2%) were level 4. Three studies associated FAI pathology with a greater pelvic incidence (PI), while four associated it with a smaller PI. Anterior pelvic tilt was associated with radiographic overcoverage parameters of FAI. In dynamic movements, decreased posterior pelvic tilt was a common feature in symptomatic FAI patients at increased hip flexion angles. FAI patients additionally demonstrated reduced sagittal pelvic ROM during dynamic hip flexion. Six studies found kinematic links between sagittal spinopelvic movement and sagittal and transverse plane hip movements. Conclusions. Our study shows that spinopelvic parameters can influence radiological and clinical manifestations of FAI, with pelvic incidence, acetabular version and muscular imbalances being aetiologically implicated. These factors may be amenable to non-surgical therapy. Individual spinopelvic mechanics may predispose to the development of FAI. If FAI pathoanatomy already exists, sagittal pelvic parameters can influence whether FAI symptoms develop and is an area of further research interest