Ongoing market challenges stalled growth for Siemens Healthcare in the company's first quarter of 2014 (end-December 31).
First-quarter revenue decreased 5% to 3.09 billion euros ($4.2 billion U.S.), compared with 3.25 billion euros ($4.42 billion U.S.) in the first quarter of fiscal 2013. When adjusted for currency changes, however, revenue rose 1%.
The healthcare division's profit also slipped 6% to 471 million euros ($640 million U.S.), compared with 503 million euros ($684 million U.S.) in the same quarter of fiscal 2013.
The company cited ongoing market challenges, including weak economic conditions in Europe, uncertainty in the healthcare market, an excise tax on medical devices in the U.S., and slowing growth in China, as reasons for the lackluster results.
![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)
