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Consistency
>90% concordance with CACS‑DRS classification grouping1
Identification
>95% correct identification of the presence of coronary artery calcifications1
Labeling
>90% accurately labeled coronary artery territories1
At a glance
Deep-learning-based automated Calcium Score
- Deep learning model automatically labels coronary artery territories.
- Deep learning model automatically segments calcifications within the coronary artery territories and provides total and per-territory scores.
- Deep learning models were developed and trained based on a variety of clinical datasets from a global population, spanning multiple scanner types and vendors.
- Ability to assign calcifications to individual coronary artery branches.
- Ability to score and label calcifications found in the aorta and cardiac valves.
CCTA 2D review
- Easily transition from Calcium Scoring to CCTA review through the integrated workflow.
- Load multi-phase data for motion analysis of chamber mobility.
- Oblique reference lines to generate views through the arteries.
- Easy access to additional rendering modes (MIP, MiniP, and average) for enhanced visualization.
- Measurement tools for distance and ROI generation.
