Start date: 01-01-2023
End date: 30-06-2023
Clinical problem
Osteartritis (OA) of the hip joint is a common condition which can lead to severe health problems. According to the RIVM, around 1.5 million people in the Netherlands suffer from OA (500 million worldwide), which is diagnosed using plain radiographs. On the radiographs, radiologists recognize OA by noticing joint space narrowing, extra bone formation (called ‘osteofytes’) and increased bone density. In a clinical setting, the number of radiographs that need OA screening is very high, and the interobserver reliability in grading the severity of OA is low to moderate.
Solution
In this project we plan to develop a deep learning algorithm for automated detection of joint space narrowing, extra bone formation and increased bone density. Besides automated detection, the algorithm must also be trained to grade hip OA severity with the Kellgren and Lawrence score. The algorithm will be externally validated in an orthopedic surgery department. We will build further on a previous deep learning algorithms developed for this purpose but which were developed with only smaller subsets of radiographs and with older deep learning algorithms.
Data
The World COACH consortium, a worldwide collaboration of 8 prospective cohorts on hip osteoarthritis include 40,555 participants (aged 35 to 80 years at baseline), of which 34,018 participants have annotated baseline pelvic radiographs available. This consortium is hosted by Erasmus MC and this project will therefore be carried out in close collaboration with Erasmus MC.
Results
When the algorithm has sufficient sensitivity and specificity it will be evaluated in practice.
