Musculoskeletal disorders are a common cause of pain and disability affecting millions of people around the world. The Orthopaedic Research Laboratory (ORL) of the Radboudumc performs research that is focused on congenital deformities of the knee. Within this spectrum patellofemoral instability due to trochlear deformity and tubercle mal-positions are common. The instability causes patellar dislocations, which is the most common acute knee disorder in children and adolescents, with negative effect on quality of life.
In an ongoing research project, musculoskeletal dynamic models based on real time dynamic CT scans are used to optimize pre-operative surgical planning for patellofemoral surgery. The goal is to optimize stability and minimize patellofemoral contact forces. With the aid of surgical navigation tools and cutting guides the pre-operative planning can be executed with high accuracy. This will hopefully lead to more satisfied patients, with less morbidities such as osteoarthritis along the way.
The RTC Deep Learning has developed a web-based application for the automatic segmentation of the patella, fibula and tibia in the dynamic CT scans of the knee. The algorithm can be used on the Grand Challenge platform. All processing is performed on the platform; there is no specialized hardware required to try out the algorithm. Running the algorithm requires an account for Grand Challenge. If you don't have an account yet, you can register on the website; alternatively, you can log in using a Google account. After registering for a new account, or logging in to an existing Grand Challenge account, you can request access to the algorithm.
