Brian Casey, Erik L. Ridley, Kate Madden Yee, and Theresa Pablos[email protected]HomeMeet the Minnies 2020 finalistsMinnies finalists, page 3September 30, 2020HomeMeet the Minnies 2020 finalistsMinnies finalists, page 2September 30, 2020HomeMeet the Minnies 2020 finalistsAre you curious about who made it to the final round in this year's edition of the Minnies, AuntMinnie.com's event recognizing excellence in radiology? This year's list of finalists shows how the COVID-19 pandemic has impacted medical imaging.September 29, 2020Page 1 of 1Top StoriesDigital X-RayLLMs excel at scoliosis detection on spine x-raysFor scoliosis detection, Grok 4 led with 94.2% accuracy.HomeExpanding opportunities for interventional radiology practiceCTWelding fumes linked to distinct lung CT changes, functionMolecular ImagingPSMA-PET linked to higher use of systemic therapiesSponsor ContentTurning Innovation into Everyday Efficiency
![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.auntminnie.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)
