Publications by authors named "Thomas Vanspauwen"
Article Synopsis
- The study aims to improve total hip arthroplasty (THA) stability by developing an AI algorithm that predicts impingement based on individual spinopelvic mechanics and patient characteristics.
- Conducted across two centers with 157 adults, the research utilized robotic technology to assess impingement during specific movements and employed the Light Gradient-Boosting Machine (LGBM) for prediction analysis.
- The results showed LGBM's prediction accuracy for impingement at 70.2%, with notable performance in estimating direction (85%) and type (72.9%), highlighting the potential of AI in enhancing THA outcomes.
Background: The objective of this study was to evaluate the correlation in measurements of the lower-limb coronal alignment between long-leg radiographs (LLRs) and computed tomography (CT) scanograms that were made during preoperative planning for robotic-arm-assisted knee arthroplasty. On the basis of published evidence demonstrating a good correlation between these imaging modalities in measuring the lower-limb mechanical axis, we hypothesized that there would be no significant differences between the 2 in the present study.
Methods: This multicenter cohort study across 3 tertiary centers included 300 patients undergoing primary robotic-arm-assisted total knee arthroplasty (TKA) or unicompartmental knee arthroplasty (UKA) for whom LLRs and CT scanograms were available preoperatively.
Introduction: Vertebral body tethering (VBT) is gaining popularity for the management of selected AIS patients. The most frequent non-mechanical complications after VBT are pulmonary complications, with a reported incidence of up to 8% for recurrent pleural effusion. However, only trace data have been published on this topic.
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