The European Federation of Radiographer Societies (EFRS) is partnering with the University of Liverpool and related organizations in the U.K., Canada, and Australia on a global virtual conference focusing on how simulation can be utilized for radiographer training during the COVID-19 pandemic.
Set to be held on 24 June, the free conference will discuss the potential role of simulation resources, techniques, and placements as temporary methods for clinical training during COVID-19 restrictions, according to the organizers. Simulation experts and researchers from around the world will share ideas for how simulation could provide some capacity to teach clinical skills in the absence of clinical placement for the duration of the pandemic restrictions.
"Clinical skills training for students in diagnostic imaging, radiotherapy and nuclear medicine has been severely impacted during the global pandemic, with the cessation of many clinical placement opportunities. Future disruption seems likely with social distancing and severely limited access to clinical departments," they noted.
The event will be split into two sessions held in the early morning and late afternoon, with a three-hour gap in between. Registration can be completed at the conference's EventBrite page. More information can also be found on Twitter.
![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)
