Mustafa Hassan[email protected]Mustafa Hassan, PhD, is a senior market analyst on the Medical Imaging Team at Signify Research.UltrasoundBeyond commoditization: How ultrasound AI vendors can thriveA new article from Signify Research discusses current market dynamics for ultrasound AI.January 29, 2026UltrasoundWireless transducers and MEMs transform ultrasoundWireless transducers and microelectromechanical systems (MEMS) are among the latest technological trends shaping the ultrasound market.November 18, 2024Page 1 of 1Top StoriesDigital X-RayDEXA confirms benefits of football on bone healthPlayers underwent a DEXA scan at the start of the season, at four months, and again at nine months.HomeRadiology's utilization problem sustainability still missedCTWelding fumes linked to distinct lung CT changes, functionMolecular ImagingHow AI can help with PET/CT guideline deviationsSponsor 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.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)
