Contributions from its acquisition of Dade Behring helped Siemens Healthcare to a 10% revenue gain in its second quarter.
For the period (end-March 31), Siemens had revenue of 2.7 billion euros ($4.3 billion U.S.), up 10% on an actual basis and 2% on an adjusted basis from the 2.5 billion euros ($3.9 billion U.S.) reported the same period a year ago. The Erlangen, Germany-based company had a net profit of 341 million euros ($533.3 million U.S.), up from 332 million euros ($519.2 million U.S.) last year.
New sales volume from the Dade Behring acquisition more than offset significant negative currency translation effects in the U.S., Siemens said. In other quarterly news, the firm's medical imaging and IT business continued to yield solid profitability despite increasing challenges in market conditions, Siemens said.
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![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)
