Addressing glenoid loosening through advanced classification and tailored glenoid component designs.

Shoulder osteoarthritis is a frequent problem in our aging population.Total shoulder arthroplasty (TSA) ,recognized for alleviating pain and enhancing function, has surged by 168% over the last decade in treating shoulder arthropathies.Given the rise in TSA procedures, it is imperative to enhance the current success rate of aTSA and decrease the burden of revision surgery. This can be achieved by addressing two specific limitations which are lack of diagnostic and prognostic classification of patients and optimal design and positioning of the glenoid component .The aim of this project is to reduce the high risk of glenoid component loosening in anatomic shoulder arthroplasty and improve clinical outcomes. To reach that we aim to classify patients based on their loosening risk, and optimize the glenoid component design and positioning for each high-risk subpopulation.My postdoctoral research project hypothesizes that: (1) The current state-of-the-art classification can be enhanced towards a loosening-risk-based classification and thereby drastically reduce glenoid component loosening. (2) A population-based FEM that characterizes the underlying glenoid component loosening mechanisms while considering the population’s variations in terms of the implant’s mechanical environment can provide novel inputs for such a classification. (3) Classification-specific optimization of the glenoid component design and positioning can address specific loosening mechanisms in high-risk patients.

Keywords:anatomic shoulder arthroplasty, Finite elemet modeling, Optimzation, surrogate finite element modeling, Classification

Disciplines:Biomedical modelling