DeepHealth has secured CE marking for its TechLive remote imaging and radiology management solution, enabling commercialization in Europe.
TechLive is a multimodality, vendor-agnostic platform that allows certified technologists to remotely operate scanners and manage imaging workflows across MR, CT, PET/CT, and ultrasound from a single consolidated interface, according to the company.
TechLiveDeepHealth
Within RadNet, TechLive's largest U.S. customer, the solution has connected more than 400 scanners and delivered a 42% reduction in MR room closure hours and a 27% increase in access to complex procedures, DeepHealth said.
TechLive has also been listed in AWS Marketplace to streamline procurement and deployment for health systems, the company noted. DeepHealth will showcase the solution at the ECR, taking place from 4 to 8 March in Vienna.
![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)
