Research
KNEE
POCUS-AI FOR KNEE OSTEOARTHRITIS
Background
Knee osteoarthritis (KOA ) is a painful and disabling joint disease, involving many structures of the knee. It is often difficult to diagnose, especially in early and mild stages, because diagnostic criteria exclude these conditions. X-ray is currently used for doubtful cases, but this imaging tool is unable to reveal most of the early-stage KOA characteristics. Improving early diagnosis ideally starts in general practice, the first step for most people with knee pain.
Ultrasound (US) provides a reliable solution to this imaging challenge. US also has the benefit that it is accessible and can even be used at the general practitioner for immediate diagnosis and start of treatment. However, this requires extensive training to acquire and interpret the ultrasound images which complicates broad adaption by General practitioners. The use of a smartphone-based Point-Of-Care UltraSound (POCUS) application with artificial intelligence (AI) for early detection of knee osteoarthritis has the potential to address this diagnostic challenge for general practicioners. This affordable AI-driven POCUS device eliminates the observer dependency and minimizes training time.
Methods
In this project, we will develop an AI-based point-of-care ultrasound device, which will enable detection of knee osteoarthritis at the general practicioner and will minimize the required training.
Funding
This research project is funded by NWO (Improving early detection of KNEE osteoarthritis with AI-driven point-of-care ultrasound: ID-KNEE study)