Osteoarthritis (OA) is the most common disorder of the musculoskeletal system and the major cause of reduced mobility among seniors. It is now considered as a disease of the whole joint organ involving the articular cartilage, subchondral bone and synovial membrane but also the menisci and ligaments. However, the underlying mechanisms through which this debilitating disease occurs and progresses have not been fully elucidated yet. Moreover, there is still no medical treatment for this pathology, and the lack of predictive biomarkers is a major obstacle to their development.
The past years have shown many studies based on the computer-aided diagnosis for knee osteoarthritis, and more recently the prediction of the disease progression. Several research teams around the world have proposed their own methods for imaging markers extraction, but yet no clinical tools have emerged for knee osteoarthritis routine evaluation. Also, the development of high-end imaging modalities (high resolution MRI, quantitative µCT, ...) highlighted deep features (anisotropic bone microarchitecture, uncorrelated multiscale changes, ...) that were not systematically transposed to the highly available modalities.
This STUDIUM consortium aims to gathers experts from several imaging areas focused on the knee osteoarthritis in order to provide a synthesis of the good practices to assess OA related imaging biomarkers. A secondary objective is to include clinicians to help explain the underlying mechanisms observed or revealed by machine learning.
The ultimate goal is to melt the data driven predictive models into an actual diagnostic aid tool for clinicians.