Vendor source
Reviewed 11 May 2026
Completeness Comprehensive (81%)
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.
Supported by 4 papers
General Information
Product name
Company
Modality
Main task
Not specified
Subspeciality
Disease targeted
Regulatory Information
Pathway:
MDD
Class:
Class IIa
Verified by Health AI Register
Pathway:
510(k) cleared
Class:
Class II
Verified by Health AI Register
Other certifications
Not specified
Evidence & Research
Peer-Reviewed Papers
Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction
Radiology · Jan 2024
The diagnostic performance and clinical value of deep learning-based nodule detection system concerning influence of location of pulmonary nodule
Insights into Imaging · Sep 2023
Added Value of Deep Learning–based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study
Radiology · Mar 2021
Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings
European Radiology · Nov 2019
Other Articles
Deep Learning-Based Automatic Chest PA Screening System for Various Devices and Hospitals, RSNA 2018
Deep Learning-Based Computer-Aided Detection System for Multiclass Multiple Lesions on Chest Radiographs: Observers’ Performance Study, RSNA 2018
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
Technical Specifications
Patient population age
Not specified
Input
Input format
Output
Output format
Integration
Deployment
Trigger for analysis
Processing time
Population
Source: Vendor | First published: Jan 15, 2024 | Last updated: May 11, 2026