Replacing the knee joint by re-creating the patient’s healthy knee
PROPEL will improve patient satisfaction after total knee replacement by restoring knee function. This is achieved by utilizing musculoskeletal models that mimic the healthy and the operated knee and place the components so that the healthy knee kinematics are obtained. Using AI this method can be applied for every patient. The PROPEL consortium consists of Radboudumc, University of Twente and Stryker, and brings together the clinical, technical and entrepreneurial skills that is required to reach the goals.
In the Netherlands, about 1.5 million people are suffering from osteoarthritis (OA), and it is expected that by 2040, this number will increase to 2.5 million (www.reuma.nl). Currently, about 30.000 TKR procedures are performed every year in the Netherlands (www.lroi-report.nl/knee/numbers). About 20-25% of TKR patients experience functional limitations in their daily activities, and they have difficulties in resuming a normal lifestyle or even returning to work. PROPEL aims to reduce the number of patients with functional limitations by 50%, and facilitate patients’ reintegration into society, enabling resumption of daily activities and return to the workplace.
The project will utilize advancements of imaging technology, automatic segmentation algorithms, and biomechanical modeling techniques to accelerate the generation of personalized musculoskeletal models. The uniqueness of the approach lies in the fact that a musculoskeletal model is made of the healthy knee of the patient (before the onset of osteoarthritis) and that its functional characteristics are re-created by placing the prosthetic components in a patient specific manner, thereby creating identical kinematics of the healthy knee. Artificial intelligence (AI)-driven surrogate modeling will replace the musculoskeletal modeling part, reducing the time required from weeks to seconds and enables clinical implementation of the method. Valorization of the project is obtained by transferring the workflow into a pre-planning system on a tablet or by directly feeding it into the robotic system.
