Advanced visualization developer Merge Healthcare and Rad-Aid International have announced a new collaboration to bring radiology and health IT to medically underserved parts of the world.
The partnership, called the Rad-Aid Merge International Imaging Informatics Initiative, or RMI4, will combine Merge's radiology information technologies with Rad-Aid's global health outreach network, which includes 3,500 volunteers, 14 country-outreach programs, 33 university-based chapters, and affiliation with the World Health Organization (WHO), the two companies said.
Nearly half the world has little or no radiology services or access to PACS, electronic health records, RIS, or hospital information systems, according to WHO. As part of the collaboration, Merge will contribute software, technical resources, and expertise in radiology image management; Rad-Aid will put these technologies to use at its international partner sites and provide clinical education, onsite training, and radiology assistance to poor and resource-limited countries.
![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)
