Hybrid population-based modeling and simulation to prevent glenoid component loosening in anatomic total shoulder arthroplasty

Anatomic total shoulder arthroplasty is the current 'gold standard' treatment for end-stage shoulder osteoarthritis patients, providing effective pain relief and improved range of motion. However, the high rate of glenoid component loosening remains a significant concern. The lack of a comprehensive diagnostic and prognostic evaluation system for preoperative planning makes outcomes heavily dependent on the surgeon's decisions. Furthermore, efforts to reduce this complication are hindered by the absence of optimized preoperative planning solutions in terms of implant positioning and optimal component designs that address the specific needs of certain patients. Recognizing that glenoid component loosening is primarily driven by mechanical factors and their interactions, this project seeks to leverage unique, readily available population-based statistical models of the glenoid's mechanical environment by integrating them in state-of-the-art hybrid in silico models. Building on these models, we aim to mitigate the high risk of glenoid loosening by 1) developing a novel loosening-based risk stratification system and 2) optimizing glenoid component positioning and design. With planned in vitro and clinical validations, this project could pave the way for a pioneering in silico platform that could transform current clinical practices in shoulder arthroplasty, ultimately enhancing patient outcomes.