Integration of dynamic imaging and symptomology to identify precise treatments for knee osteoarthritis
Knee osteoarthritis (OA) can present in different ways. The relationships between the symptoms and their causes are mostly unknown, making it difficult to choose the best course of action. Being able to identify various contributor to symptoms would be extremely beneficial to determining the optimal course of treatment. We will develop a system that accounts for 3D joint movements, forces, and their relationships to symptoms using state-of-the-art technology. Using artificial intelligence, we can combine information from advanced imaging modalities to understand knee joint mechanics while linking this information to patient symptoms. This will allow for precise understanding of the cause of symptoms. We will assess 35 participants and build machine learning algorithms to understand the relationship between patient symptoms and mechanics. We will then build an application to be used in the Fowler Kennedy Sports Medicine Clinic to advance the precision of care and outcomes for knee OA.