CorEx is artificial intelligence software for cardiac imaging. It prioritizes at-risk patients. It automatically analyzes coronary CT images (CCTA) images independently detecting and classifying coronary lesions quantitatively. CorEx automatically classifies stenosis according to the international CAD-RADS classification. CorEx aids the assessment of artery damage and the severity of the obstruction. It also provides an FFR AI prediction: for each coronary the solution predicts if the threshold of 80% is reached or not.
Product specifications Information source: Vendor
Last updated: Jan. 19, 2023
Product name CorEx
Company Spimed-AI
Subspeciality Cardiac
Modality CT
Disease targeted Coronary stenosis
Key-features Stenosis classification according to CAD-RADS, FFR-AI prediction per coronary, confidence score, risk prioritization
Suggested use Before: adapting worklist order
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion
After: diagnosis verification
Data characteristics
Population Should not be used under the age of 12 years
Input CCTA: MPR curvilinear images / 40° for the 3 main arteries (27 images)
Input format DICOM
Output Draft radiology report, worklist order, image annotations, risk score, table of quantified values, 3D visualization of coronary arteries with stenosis map
Output format PDF
Integration Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
Trigger for analysis On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 1 - 10 minutes
Certified, Class I , MDD
No or not yet
Market presence
On market since 03-2021
Distribution channels
Countries present (clinical, non-research use) EU
Paying clinical customers (institutes)
Research/test users (institutes) 35
Pricing model Pay-per-use, Subscription
Based on Number of analyses, number of CT scanners using application
Peer reviewed papers on performance

  • Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection (read)

Non-peer reviewed papers on performance
Other relevant papers