Segmed Blogs

  Knee osteoarthritis is a serious and common joint disease that can cause pain, functional limitations, reduced mobility, and lower quality of life. Predicting which patients are likely to progress is important because it could help clinicians identify high-risk individuals earlier and guide more personalized treatment strategies. Currently, knee osteoarthritis diagnosis and progression assessment rely largely on radiographic imaging, clinical symptoms, and established clinical markers. However, accurately predicting which patients will experience worsening structural damage, worsening pain, or both remains challenging.‍ Predicting knee osteoarthritis progression using neural network with longitudinal MRI radiomics, and biochemical biomarkers: A modeling study Wang T, Liu H, Zhao W, Cao P, Li J, Chen T, Ruan G, Zhang Y, Wang X, Dang Q, Zhang M, Tack A, Hunter D, Ding C, Li S. Predicting knee osteoarthritis progression using neural network with longitudinal MRI radiomics, and bioch...

A large-scale analysis of 33.7 million radiology reports uncovers how rarely radiologists independently document oligometastatic disease and why that matters for patient outcomes. At ECR 2026 , the European Congress of Radiology in Vienna, the world’s second-largest radiology congress, one of Segmed’s studies stood out . Among 11,376 submitted abstracts, an observational study through real-world data analysis of oligometastatic disease (OMD) documentation in routine U.S. radiology practice was recognized with the Best Research Presentation Abstract Award, on the topic of Oncologic Imaging. For a field still debating whether AI or structured reporting will close the gap between imaging interpretation and clinical decision-making, this study reframes the question entirely. The gap is not technological: it is linguistic, habitual, and measurable. And it is happening right now, in routine practice, across millions of reports. This blog breaks down what the study found, why it matters, and...