
It's clear there's no quick fix to the pandemic, Morag Stout, a consultant sonographer at NHS Greater Glasgow and Clyde, U.K., has written in an article posted on the Society and College of Radiographers (SCoR) website. "This pandemic is not a short, sharp shock; it is a marathon," she noted.
As hospitals continue to adjust to the current situation, the challenges of social distancing, personal protective equipment (PPE) availability, staff shielding, limited access to equipment and waiting areas, and patient anxiety and staff morale are important considerations. The volume of imaging requests is returning to near prepandemic levels with no signs of abating, but now staff are adapting to the high demand with different safety protocols involved, she continued.
Morag Stout, along with her sonographer colleagues. Image courtesy of the Society and College of Radiographers.The pandemic necessitated imaging urgent or semiurgent cases, but there's also an imaging backlog, which sonographers are skeptical they'll be able to clear. To deal with the problem, the Scottish government has advocated for a national sonographer bank to allow for cross-boundary working and to reduce the need for locums. However, it is unclear if this idea will come to fruition, according to Stout.
In addition, the pandemic has revealed to the public the vital role medical staff play and demonstrates the need for respect and appreciation, she added. Governments must now invest in health systems, especially regarding diagnostics, otherwise they'll have to deal with the consequences of delayed diagnoses or treatment.











![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)





