The U.K. Society of Radiographers (SoR) and the Royal College of Radiologists are among the medical groups demanding urgent consideration of sonographer regulation to meet current and future demands for ultrasound.
In a letter to the Secretary of State for Health and Social Care, the SoR welcomed the news of additional National Health Service (NHS) funding for diagnostic imaging equipment in the October budget. But the society warned that current models of education and recruitment are unable to deliver the workforce at the pace and scale needed to meet increasing demands and the backlog of examinations.
Development of innovative educational programmes of study for sonographers in the U.K. has stalled due to the lack of statutory regulation, widening the gap between current staffing levels and workforce needs, the SoR said.
In addition, sonography regulation was last considered in the Professional Standards Authority (PSA) report, published in 2019, according to the SoR. The PSA concluded that sonographer regulation should be reviewed when there is a need or an increase in nonregistered sonographers from international and local recruitment.
"We believe that time is now," said SoR chief executive Richard Evans.
The other signatories included the Association of Child Protection Professionals, the British Medical Ultrasound Society, the British Society of Echocardiography, the College of Radiographers Patient Advisory Group, the Consortium for the Accreditation of Sonographic Education, the Royal College of Midwives, the Royal College of Obstetricians and Gynecologists, and the Society of Vascular Technologists.
For full details, go to the SoR website.
![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)
