Negative currency effects adversely impacted financial results for Siemens Healthineers in the first quarter of the parent company's 2018 fiscal year (end-December 31).
Revenue for Siemens' healthcare segment decreased 4% before currency adjustments to 3.196 billion euros ($3.95 billion U.S.), compared with 3.326 billion euros ($4.14 billion U.S.) in the first quarter of the 2017 fiscal year. After adjusting for currency effects, revenue would have risen 2%.
Profit for Siemens Healthineers also slipped by 15% to 541 million euros ($672 million U.S.), compared with profit of 638 million euros ($791 million U.S.) in the first quarter of 2017.
Orders fell by 5% in the first quarter to 3.356 billion ($4.15 billion U.S.), compared with orders of 3.521 billion euros ($4.36 billion U.S.) in the first quarter of fiscal 2017.
The parent company blamed negative currency effects in part for the lackluster results. On a geographic basis, the division saw revenue growth mainly in China, the company said.
Siemens also noted that it is proceeding with its initial public offering (IPO) for Healthineers, but it provided no timetable for its commencement.
![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)
