Dear AuntMinnieEurope Member,
The absence of face-to-face congresses has forced everybody to be even more creative over the past 18 months or so, particularly when it comes to training and education.
Take the example of the Young Club of the European Society of Breast Imaging. The club's committee asked senior officials to provide their top three tips on breast imaging, and the wide range of responses make for fascinating reading. Find out more in the Women's Imaging Community.
Artificial intelligence (AI) still needs to improve before it can outperform breast radiologists or even be used in clinical settings, according to new U.K. research.
AI might alter the spectrum of disease detected at breast screening if it differentially detects more microcalcifications, and this may increase rates of overdiagnosis and overtreatment, the authors wrote. They also said that AI algorithms don't understand the context, mode of collection, or meaning of images being viewed.
Dr. Raman Uberoi is a well-known figure in European radiology. In a revealing interview, he spoke about not only the problems ahead for radiology but also his career highlights and the future role of the U.K. Royal College of Radiologists.
In this week's second personal profile, we have a special feature about Dr. Michiko Dohi, the leading sports physician from Japan. She commented on the use of imaging in elite sport and what it takes to be a successful team doctor. She also elaborated on her own work.
Looking ahead, ECR is only six months away, and the preliminary program is now available and registration has opened. Make sure you take a glimpse at what the organizers have in store -- both onsite and online.
![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)
