
In response to the findings of its 2020 training and education survey posted earlier this month by Insights into Imaging, the European Society of Radiology (ESR) has released four core pledges for the future:
- To support clinical and subspecialty training and maintenance of competencies through its educational and accreditation activities, through subspecialty member Societies, European School of Radiology (ESOR), European Diploma in Radiology (EDiR), and other ESR-endorsed subspecialty diplomas.
- To develop patient communications strategy with recommendations for enhancing patient communications training as part of ESOR/ESR courses, meeting the needs of patients with resources for patient information prior to imaging investigations, and reflecting on future ways of providing results, through partnership with the ESR Patient Advisory Group (ESR-PAG) and National Societies Committee, and developing a framework for transparent communication and learning from errors and discrepancies.
- To develop recommendations and guidance to support best practice in multidisciplinary teamwork, to enable the integration of clinical and imaging information for the benefit of patient care, while being mindful of pressures on limited radiology resources.
- To harness the enthusiasm of radiologists by continuing to support and highlight radiology-led teaching and research opportunities through the scientific committees, meetings, and publications of the ESR.











![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)





