MRI detected a high prevalence of abnormalities associated with knee osteoarthritis in middle-aged and elderly patients who had no evidence of the condition on x-ray, according to a study published online on Wednesday in the British Medical Journal.
Researchers from Boston University School of Medicine, Brigham and Women's Hospital in Boston, Lund University in Sweden, and Klinikum Augsburg in Germany evaluated right knee MRI scans of 710 ambulatory patients from the Framingham Osteoarthritis Study.
The review showed that approximately 90% of knees that showed no signs of osteoarthritis on x-ray exams displayed clear signs of osteoarthritis with MRI. In addition, MRI abnormalities were highly prevalent even in patients with no knee pain, suggesting that MRI is not useful in this age group to evaluate knee pain.
MRI could be detecting early osteoporosis; however, further research is needed to determine how many of the patients will later be diagnosed with knee osteoporosis, said lead study author Dr. Ali Guermazi, PhD, a professor of radiology at Boston University School of Medicine. MRI would be too expensive to perform as a routine imaging investigation, the authors noted.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
