The governments of the U.S. and the U.K. have signed an agreement designed to improve the use and sharing of healthcare information technology and tools.
The agreement represents a formal commitment between the countries to collaborate to advance the applications of data and technology to improve the quality and efficiency of healthcare delivery. The deal was signed by U.K. Secretary of State for Health Jeremy Hunt and U.S. Health and Human Services Secretary Kathleen Sebelius.
The agreement is the outcome of a bilateral summit held in June 2013, in which the two countries agreed to collaborate in four key areas:
- The sharing of quality indicators and the identification of best practices between different British and U.S. quality indicators
- The "liberation" of data by finding areas of collaboration around open data and safe and secure data transparency of secondary stored data
- Adoption of digital health record systems
- The priming of the health IT market by identifying barriers to innovation and supporting small and medium-sized enterprises and start-ups
The full text of the memorandum is available here.
![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)
