Agfa-Gevaert Group, parent company of Agfa HealthCare, is negotiating the sale of part of its healthcare IT business to Dedalus Holding.
On 14 May, Agfa's board of directors started investigating the sale of its healthcare segment and today the negotiation process has begun. Dedalus Holding would acquire 100% of the business at an enterprise value of 975 million euros, subject to regular working capital and net debt adjustments.
The business consists of healthcare information solutions, integrated care activities, and imaging IT to the extent these activities are integrated into healthcare information solutions, which happens mainly in Austria, Germany, Switzerland, France, and Brazil.
The transaction is subject to customary employees' consultations, regulatory approvals, and closing conditions. It is expected that, upon positive conclusion of the negotiations, the transaction will be completed in the second quarter of 2020.
"The expected sale of the business, which generates around 260 million euros of full year revenues, will represent another milestone in our transformation process," said Christian Reinaudo, CEO of the Agfa-Gevaert Group, in a press release. "We are looking forward to this important step. We believe that under Dedalus Holding's ownership, the business will continue to develop into a leading pan-European player in the healthcare IT market."
Going forward, Agfa HealthCare will focus on imaging IT, with the intention to grow revenues and raise the earnings before interest, taxes, depreciation, and amortization (EBITDA) margin performance over time from midsingle-digit percentage of revenues to a double-digit level, Reinaudo said.
![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)
