VUNO Med®-Chest X-ray™

VUNO
VUNO Med® Chest X-Ray™ is a deep learning-based screening solution for five major lung diseases - Nodule/Mass, Consolidation, Interstitial Opacity, Pneumothorax, Pleural Effusion - on chest X-ray (PA/AP) images. This algorithm provides information on the presence of the abnormalities, their names, abnormality scores, and locations.
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Information source: Vendor
Last updated: January 15, 2024

General Information

General
Product name VUNO Med®-Chest X-ray™
Company VUNO
Subspeciality Chest
Modality X-ray
Disease targeted Nodule/Mass, Consolidation, Interstitial Opacity, Pneumothorax, Pleural Effusion
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

Technical Specifications

Data characteristics
Population All population with a risk of thoracic abnormalities
Input Chest XR PA/AP images
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

Regulatory

Certification
CE
Certified, Class IIa , MDD
FDA No or not yet
Intended Use Statements
Intended use (according to CE)

Market

Market presence
On market since 06-2020
Distribution channels Tempus Pixel, Samsung Electronics
Countries present (clinical, non-research use) 10+
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Pay-per-use, Subscription
Based on Number of analyses

Evidence

Evidence
Peer reviewed papers on performance

  • Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction (read)

  • The diagnostic performance and clinical value of deep learning-based nodule detection system concerning influence of location of pulmonary nodule (read)

  • Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings (read)

  • Added Value of Deep Learning–based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study (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)