Dear AuntMinnieEurope Member,
The development of viable AI technology remains frustratingly slow in some areas of clinical practice, but Alzheimer's disease identification is one field in which the software appears to be relatively advanced and there is good reason for optimism.
This week's top article provides a succinct and readable summary of how exactly AI can improve the care of Alzheimer's patients. Significantly, the authors won a prestigious magna cum laude award at ECR 2024 for their work.
How can you best avoid oversights in emergency use of CT? A group of trauma imaging experts have addressed this question and provided a list of 10 recommendations to improve radiologists' performance.
There's no room for racism in the workplace: that's the clear message sent out by two recent tribunals. In one case, a radiologist who trained in Antwerp, Belgium, was given a three-month suspension, and in another, a U.K. radiographer was struck off.
Ultrasound plays an increasingly valuable role in the diagnosis of respiratory conditions in newborns. Researchers from Paris have published new results in this area.
It's a busy time for congresses right now. Check out our report from the 37th national congress of the Spanish Society of Medical Radiology (SERAM), held in Barcelona from 22 to 25 May.
Philip Ward
Editor in Chief
AuntMinnieEurope.com
![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)
