CorEx

Spimed-AI

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

General
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
Technology
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
Certification
CE	Certified, Class I , MDD
FDA	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
Pricing model	Pay-per-use, Subscription
Based on	Number of analyses, number of CT scanners using application
Evidence
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