German industrial giant Siemens reported growth in sales and profit at its Siemens Healthcare division this week, but said that the healthcare market's outlook remains poor.
For the period (end-March 31), revenue in the healthcare unit grew to 2.984 billion euros ($3.952 billion), up 10% on an actual basis and up 4% after adjusting for currency changes, compared with sales of 2.722 billion euros ($3.605 billion) in the same period the year before. Orders grew 6% in the period.
The division's sector profit rose 4% to 355 million euros ($470.2 million), compared with 341 million euros ($451.7 million) in the second quarter of 2008. The company said that its Imaging and IT division was a top earnings performer, with second-quarter profit rising to 265 million euros ($351.2 million).
In reporting the results, Siemens said that tight credit continues to constrain market growth, especially in the U.S. and Japan. Growth in Australia, Asia outside Japan, Europe, and the Middle East offset the weakness. Siemens said that the division "expects continued deterioration in market conditions" in the healthcare industry.
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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)
