VUNO Med®-LungCT AI™

VUNO

It detects and quantifies pulmonary nodules, providing size, volume, nodule type, location, calcification, and spiculation. An automatic report based on the calculated Lung-RADS category is produced to assist in managing pulmonary nodules. Its follow-up registration and nodule matching aid the comparison of serial CT scans. Operation settings can be customized between sensitivity-oriented for high-risk patients and specificity-oriented for efficient screening.
Product specifications Information source: Vendor
Last updated: July 12, 2020
General
Product name VUNO Med®-LungCT AI™
Company VUNO
Subspeciality Chest
Modality CT
Disease targeted Lung Cancer
Key-features Nodule detection, Nodule measurement, Nodule classification, Lung-RADS reporting
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
Data characteristics
Population All lung cancer screening population
Input Low-dose Lung CT Scan
Input format DICOM
Output List of detected nodules, Nodule diameter/volume, Nodule types, Nodule growth
Output format DICOM, PDF
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 10 - 60 seconds
Certification
CE Certified, Class IIa
FDA No or not yet
Market presence
On market since 04-2020 (CE 06-2020)
Distribution channels
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
Non-peer reviewed papers on performance
Other relevant papers

  • Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer(read)

  • Residual CNN-based Image Super-Resolution for CT Slice Thickness Reduction using Paired CT Scans: Preliminary Clinical Validation (read)

  • A Deep Learning-based CAD that Can Reduce False Negative Reports: A Preliminary Study in Health Screening Center (read)