Publications by authors named "Fabrice Glod"
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.
Hip, spine, and pelvis function as a unified kinetic chain. Any spinal pathology, results in compensatory changes in the other components to accommodate for the reduced spinopelvic motion. The complex relationship between spinopelvic mobility and component positioning in total hip arthroplasty presents a challenge in achieving functional implant positioning.
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- Accurate implant positioning in Total Hip Arthroplasty (THA) is crucial for patient stability and range of motion, and should be personalized due to variations in each patient's biomechanics and spinopelvic mobility.
- The manuscript discusses challenges related to spinopelvic imbalance and introduces a systematic method for achieving functional-component positioning in surgery.
- Results from robotic-assisted THA indicate that using this technology improves planning accuracy, preserves important anatomical parameters, and enhances patient satisfaction and clinical outcomes.