Computer-aided detection developer Rcadia Medical Imaging of Haifa, Israel, has received the European CE Mark for its COR Analyzer, its proprietary software that helps identify patients with significant coronary artery disease by automatic analysis of coronary CT angiography (CTA) studies.
COR Analyzer is designed to process patient images from coronary CTA studies generated by all four manufacturers of 64-slice (and above) CT systems and produce results in real-time that determine whether significant lesions are present in major coronary arteries.
The software has already been cleared by the U.S. Food and Drug Administration.
Related Reading
Rcadia lands investment, December 16, 2008
Rcadia debuts COR Analyzer for coronary CTA, December 3, 2008
Rcadia adds distributor, November 20, 2007
Rcadia establishes U.S. beta site, October 30, 2007
Rcadia nets FDA nod, September 26, 2007
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![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)
