Philips Healthcare reported a 7% increase in sales on a currency-adjusted basis in its 2013 fourth quarter, as the division's profitability improved as well.
For the period (end-December 31), the healthcare division had sales of 2.828 billion euros ($3.862 billion U.S.), compared with 2.918 billion euros ($3.933 billion U.S.) in the fourth quarter of the previous year. After adjustment for currency fluctuations, sales grew 4%.
The division saw growth in the midsingle digits in imaging systems and low-single-digit growth in patient care and clinical informatics. By region, North American orders dropped 6%, while European orders grew 3% and growth geographies (everything outside of the U.S., Canada, Western Europe, Australia, New Zealand, South Korea, Japan, and Israel) climbed 7%.
Earnings before interest, taxes, and amortization (EBITA) reached 541 million euros ($739.1 million U.S.), up from 411 million euros ($561.5 million U.S.) in the fourth quarter of 2012. The increase was driven by overhead cost reductions, according to the company. EBITA for the year was 1.512 billion euros ($2.065 billion U.S.), compared with 1.226 billion euros ($1.675 billion U.S.) in 2012.
For the year, the healthcare division produced revenues of 9.575 billion euros ($13.086 billion U.S.), down 4% from 2012's 9.983 billion euros ($13.458 billion U.S.).
Philips cited a number of accomplishments during the quarter, including the launch of its Vereos digital PET/CT system and work-in-progress IQon CT scanner.
On the other hand, the company reported that a U.S. Food and Drug Administration (FDA) inspection at its facility in Cleveland discovered "certain issues" in manufacturing process controls. On January 10 the company voluntarily suspended new production at the plant as it works to strengthen manufacturing processes.
The stoppage will negatively affect the healthcare sector's EBITA of 60 to 70 million euros ($82 to $95.6 million U.S.) in the first half of 2014, which the company expects to recover in the second half of the year.
![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)
