In the realm of cutting-edge medical research,
collaboration and innovation are key drivers of progress. Recently, we had the privilege of sitting down with Dr. Jansen, a distinguished technical physician and assistant professor at UMC Utrecht, to delve into the fascinating world of joint research and medical imaging. Dr. Jansen's expertise bridges the gap between medical knowledge and advanced technologies, particularly in the field of musculoskeletal diseases. In this interview, we explore Dr. Jansen's groundbreaking study on knee joint distraction treatment for osteoarthritis, the pivotal role of AI in clinical trials, and the promising future of predictive radiographic analysis. Let's journey into the depths of scientific discovery.
Interview:
Annalisa for ImageBiopsy Lab: Hi Dr. Jansen, first of all, thank you so much for taking the time for this little interview. We have prepared a couple of questions and are looking forward to hearing your answers, so let’s jump right into it.
Could you please tell us something about your background and your research focus?
Dr. Jansen: I am a technical physician, combining medical knowledge and skills with expertise in technologies relevant to healthcare, such as medical imaging. In my PhD, I used medical imaging to better understand processes happening in the joint as a result of knee joint distraction treatment for osteoarthritis. Now, as an assistant professor, my focus is imaging of joint degeneration and repair in musculoskeletal diseases.
Annalisa: Your team recently published a study using IB Lab KOALA, our knee osteoarthritis labeling assistant, to assess the association between radiographic changes and clinical improvement after Knee Joint Distraction. Would you mind giving us some details about the study design and its conclusion?
Dr. Jansen: In that study, we evaluated sixteen patients treated with knee joint distraction, comparing radiographic outcomes using KOALA, patient-reported clinical outcomes, and changes in synovial fluid markers during treatment. We found improvements in pain, Kellgren-Lawrence grades, and joint space width, marking a significant association between joint space width improvement and pain reduction. These results, for the first time, were linked with synovial fluid marker changes, suggesting a potential avenue for disease-modifying osteoarthritis treatments.
Annalisa: What impact do you think these outcomes could have in the future?
Dr. Jansen: These results could aid in selecting disease-modifying osteoarthritis treatments, offering insights into joint repair mechanisms. AI-based measurement methods, like KOALA, might revolutionize imaging analysis in clinical trials, providing robust results associated with pain improvements.
Annalisa: ImageBiopsy Lab is gaining traction in the CRO business, and collaborations like ours provide valuable insights. What benefits did you draw from the collaboration?
Dr. Jansen: The collaboration allowed objective evaluation of knee radiographs not performed in regular clinical care. KOALA's analysis provided insights into radiographic changes, synovial fluid markers, and potential pathways for joint regeneration. It was a seamless collaboration, leading to valuable knowledge and a publication in Rheumatology.
Annalisa: Without spoiling too much, your team in Utrecht is preparing another study using IBLab AI algorithms. Can you give us a brief insight into what we can expect?
Dr. Jansen: We're planning a study using IBLab’s algorithms to predict knee osteoarthritis development and progression from radiographs in an early phase. It's a work in progress, so stay tuned for updates!
In the pursuit of advancing medical knowledge, collaborations like these pave the way for groundbreaking discoveries. Dr. Jansen's insights into the intersection of AI and musculoskeletal research offer a glimpse into a future where innovative technologies enhance our understanding and treatment of complex diseases. Stay tuned for more updates as the world of medical research continues to unfold.
↳ Interested in trying IB Lab KOALA yourself?