Optimizing Total Hip Arthroplasty using Predictive Dynamic Simulation of Human Sit-to-Stand Movement and Deep Learning for Flexible Spinopelvic Model Identification
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Date
2024-01-29
Authors
MohammadiNasrabadi, AliAsghar
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Recent studies have highlighted the significance of spine stiffness and spinopelvic measures in the analysis of lower limb dynamics during human motion. These factors are essential for optimizing surgical planning, especially in total hip arthroplasty (THA) procedures. Their critical role is particularly evident in individuals who have prior spine fusion surgery undergoing THA. However, the mechanism through which these parameters impact human movement remains to be fully elucidated.
This thesis aims to bridge this knowledge gap by introducing a novel approach to optimizing THA. It involves integrating an innovative deep learning model, LanDet—an anatomical landmark detection algorithm—with a predictive human dynamic model for surgical planning. This integration is crucial in considering the often-neglected aspects of spine stiffness, pelvic tilt, and spinopelvic measures, especially in patients with previous spine fusion surgery. The LanDet model streamlines the extraction of spinopelvic metrics, thus improving the efficiency and accuracy of surgical planning. It also provides essential data to assign suitable stiffness parameters for the predictive simulation model. The predictive model is then utilized to optimize hip cup implant orientation, tailored for each specific spinopelvic condition. This approach ensures a more refined patient categorization system than currently exists, and enables surgeons to optimize hip implant orientation, thereby minimizing the risk of implant impingement and potential dislocation post-surgery.
The first part of the thesis introduces LanDet, explaining its role in enhancing surgical planning efficiency and precision. LanDet not only expedites the extraction of spinopelvic metrics using a physics-informed deep learning model but also supports a refined patient categorization system. It provides necessary data for assigning suitable stiffness parameters in the predictive model, which is critical for individualizing THA procedures. LanDet demonstrates performance improvements and addresses current challenges in anatomical landmark detection, such as the misdetection of adjacent or similar landmarks.
Building on LanDet's capabilities, the second part of the thesis concentrates on optimizing the orientation of the cup implant. This includes an analysis of factors like spine stiffness and pelvic tilt, vital in evaluating the risk of implant impingement and dislocation after THA surgery. Through two case studies involving motion capture and radiographic analysis, the research illustrates the impact of varying spinopelvic conditions on lower limb motion during daily activities, such as the sit-to-stand movement. A significant achievement of this research is the development of a dynamic human model incorporating detailed spine elements. This research pioneers the use of predictive dynamic modeling in this context, refining cup implant orientation by considering patient-specific spinopelvic conditions. It establishes a direct correlation between spine stiffness, pelvic tilt, and the risk of implant impingement, providing guidance and insights for surgeons in pre-operative planning.
In summary, this research introduces an end-to-end approach to optimize THA surgery outcomes, which can be extended to investigate the impact of diverse pathological conditions, not addressed in this specific research, on optimized implant positioning. This can advance THA and other surgical procedures, contributing to increased patient satisfaction and delivering possible time and cost savings to healthcare systems.
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Keywords
predictive simulation, spine stiffness, deep learning, physics-informed model, optimization, THA