Vincent ChanMarket Analysis3 AI-based technology trends on display at the 2023 RSNA conferenceVincent Chan of Carestream predicts technology trends for AI in digital radiography at RSNA 2023.November 22, 2023Page 1 of 1Top StoriesMRIWhole body MRI, deep-learning can 'map' distribution of fat and muscleThe map shows that the quality and amount of skeletal muscle, not just visceral fat, are strong predictors of disease.MRIPodcast: Twisted litigation of fatal MRI accident at open MRI centerBreastAI triage flags half of screen-detected cancers in top 2% of scansRadiation Oncology/TherapyWill upright radiotherapy become a new standard?Sponsor 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&fit=crop&h=400&q=70&w=600)
