Cardio AI

Arterys

Arterys Cardio AI is a web-accessible image post-processing analysis software device used for viewing and quantifying cardiovascular MR images. The device is intended to visualize and quantify MRI data in DICOM format. Manual and semi-automatic border detection forms the basis for analysis. The software has features for loading, saving, generating screen displays, and aggregating quantitative data from cardiovascular images acquired from magnetic resonance(MR) scanners.
Product specifications Information source: Vendor
Last updated: Aug. 21, 2020
General
Product name Cardio AI
Company Arterys
Subspeciality Cardiac
Modality MR
Disease targeted cardiac diseases
Key-features LV and RV segmentation, semi-quantitative perfusion, quantify scarring
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion
Data characteristics
Population all cardiac MRI including 4D flow
Input Cardiac MR from 1.5T and 3.0T machines
Input format DICOM
Output color mapping, segmentation contours and quantifications
Output format
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased
Deployment Cloud-based
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time
Certification
CE
Certified, Class IIa
FDA
 510(k) cleared, Class II
Market presence
On market since 2013
Distribution channels Arterys marketplace
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Pay-per-use, Subscription
Based on
Evidence
Peer reviewed papers on performance

  • Clinical Performance and Role of Expert Supervision of Deep Learning for Cardiac Ventricular Volumetry: A Validation Study (read)
Non-peer reviewed papers on performance
Other relevant papers

  • Transforming Cardiac MR: Advances in AI, 4D Flow and Cloud Computing (read)