Osteoarthritis (OA) is the most common musculoskeletal joint disease, in which the articular cartilage and subchondral bone are degenerated, causing unbearable pain within the joint. OA affects more than 250 million people and places a huge financial burden on both the patient (12k€/year) and the healthcare systems (7.2bn€/year). These costs are likely to increase due to several factors: population growth, aging because of increased life expectancy, and increased prevalence of obesity. Despite research efforts, there is no cure for OA and thus by far, the best and most cost-effective treatment option would be disease prevention.
Finite element (FE) models have been used to predict knee joint OA, by assessing altered joint biomechanics or excessive joint and tissue loading. For FE models to be used as a clinical tool to predict knee OA, several improvements are needed.
PLM is regularly used in the Life Science industry to address various business challenges, integrating research, development, quality, and regulatory compliance. This increases the efficiency and flexibility in partnerships and networks in a strictly regulated market with high demands for transparency, data consistency, and traceability. Examples of such successful integrations of PLM in Life Sciences are Orion and Cytiva. Why not in the Life Science academic research?
In this presentation, Paul Bolcos will showcase why PLM would be a great asset in academic research and illustrate how simulations can be integrated with the PLM cycle. For this he will present a few FE modeling approaches from his Ph.D. work as well as from colleagues, focusing on methodology development, verification/validation, and automation for large-scale use.