
A growing number of confrontations have been occurring in the U.K. during prenatal scans due to new rules preventing birth partners from attending the appointments over the risk of COVID-19 infection. The confrontations have been condemned by the Society of Radiographers (SoR).
SoR is demanding NHS Trusts and Health Boards condemn the attacks, in which partners and friends of patients have verbally abused sonographers and refused to wait outside the department until called into scan rooms or wear face coverings or stay in designated seats or behind screens. A draft letter by SoR asks local authorities to condemn these actions, stated a 4 November report on the SoR website.
The rules were adopted during the initial COVID-19 lockdown as a way of preventing transmission of the SARS-CoV-2 virus. A recent study estimated that three-quarters of NHS hospitals were not allowing birth partners to support pregnant women during the entire course of their labor; they are thus forced to go to their appointments alone, according to a recent article in the Guardian.
Populist groups have encouraged the public to challenge U.K. national guidelines on the scanning rules, rallying supporters using the social media hashtag #ButNotMaternity. The campaign seeks to require NHS Trusts to allow birth partners to be present during all pregnancy scans and appointments at all stages of labor.
The issue reached new heights when SoR learned that the groups were attempting to wage legal challenges against NHS Trusts that continue to adhere to restrictions in the guidelines that relate to cases in which local risk assessments suggest that it is unsafe for partners to remain with pregnant women.












![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&fit=crop&h=112&q=70&w=112)





