VUNO Med®-Chest X-ray™

VUNO

It informs the reader about major thoracic findings and its location. The current version of VUNO Med-Chest X-ray can find nodule/mass, consolidation, interstitial opacity, pleural effusion, and pneumothorax.
Product specifications Information source: Vendor
Last updated: July 12, 2020
General
Product name VUNO Med®-Chest X-ray™
Company VUNO
Subspeciality Chest
Modality X-ray
Disease targeted Major Thoracic Disease
Key-features Abnormality detection
Suggested use Before: adapting worklist order, flagging acute findings
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion
Data characteristics
Population All population with a risk of thoracic abnormalities
Input Chest PA image
Input format DICOM
Output Abnormality score, Lesion heatmap, Lesion boundary
Output format DICOM, GSPS
Technology
Integration Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based, Hybrid solution
Trigger for analysis Automatically, right after the image acquisition
Processing time < 3 sec
Certification
CE
Certified, Class IIa
FDA
 No or not yet
Market presence
On market since 09-2019 (CE 06-2020)
Distribution channels Arterys
Countries present (clinical, non-research use) 10+
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Pay-per-use
Based on Number of analyses
Evidence
Peer reviewed papers on performance

  • Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings (read)
Non-peer reviewed papers on performance
Other relevant papers

  • Deep Learning-Based Automatic Chest PA Screening System for Various Devices and Hospitals, RSNA 2018 (read)

  • Deep Learning-Based Computer-Aided Detection System for Multiclass Multiple Lesions on Chest Radiographs: Observers’ Performance Study, RSNA 2018 (read)

  • Evaluation of the Performance of Deep Learning Models Trained on a Combination of Major Abnormal Patterns on Chest Radiographs for Major Chest Diseases at International Multi-centers, RSNA 2019 (read)