Last Updated:
2 September 2021
VUNO Med-Chest X-ray is an artificial intelligence (AI)-based diagnostic supporting solution for chest X-ray. The solution accurately and instantly detects five of the most common thoracic findings such as nodule/mass, pneumothorax, interstitial opacity, pleural effusion, and consolidation and can therefore support doctors with the diagnosis of major lung diseases such as lung cancer, tuberculosis (TB) and pneumonia based on a combination of the findings.
In addition, VUNO Med-Chest X-ray Pro, which can detect TB and pneumonia, including more than 11 abnormal findings, has been developed and will be released in 2022.
Demo site: http://stoptb-demo.vunomed.com:5010
ID: admin@vuno.co
Password: Vuno2020!
Certification
VUNO Med-Chest X-ray: MFDS (K-FDA) approved, CE certified
VUNO Med-Chest X-ray Pro: MFDS
Development Stage
Vuno Med-Chest X-ray: On the market
VUNO Med-Chest X-ray Pro: Under development
Deployment
On-device (Offline)
On-premise (Intranet)
Cloud (Online)
Intended Age Group
19+ years
Target Setting
Primary health centres, general hospital (above primary level), teleradiology companies, government/public sector, e.g. national TB programme, private sector
Current Market
Asia, Europe
Input
»» Chest X-ray image format: DICOM
»» Chest X-ray type: posterior-anterior chest X-ray, anterior-posterior chest X-ray
Output
»» Heat map, abnormality score for each abnormality, location of each abnormality, outline
»» Lung findings identified by AI:
VUNO Med-Chest X-ray: consolidation, interstitial opacity, mass/nodule, pleural effusion, pneumothorax,
VUNO Med-Chest X-ray Pro: nodule/mass, consolidation, interstitial opacity, pleural effusion, pneumothorax, atelectasis, calcification, rib fracture, mediastinal widening, pneumoperitoneum, cardiomegaly, TB, pneumonia
»» Lung findings included in TB score:
consolidation, interstitial markings, mass, nodule
»» Disease scores identified (detection of other diseases on chest X-ray beyond TB could come with additional cost):
pneumonia
»» Format: JPEG, PNG, DICOM, JSON
Hardware
Requirements can vary depending on the business model. Please contact business@vuno.co for further information.
Validation
Not needed, the solution has studied images taken with imaging equipment from 15+ global vendors. This coverage is expanding. Please contact business@vuno.co for further information.
Hardware
Requirements can vary depending on the business model. Please contact business@vuno.co for further information.
Server
VUNO Med-Chest X-ray leverages Amazon Web Services Cloud for the online product. A local or national server can be set up if required.
Integration
It is possible to integrate the product with the client’s legacy picture archiving and communication system (PACS) at no additional fee.
Software
Please contact business@vuno.co for further information.
Processing Time
It takes less than 5 seconds to analyse one DICOM image.
Data Sharing & Privacy
»» Server location (for online product)
VUNO Med-Chest X-ray leverages Amazon Web Services Cloud for online product. A local or national server can be set up if required.
»» Data are not shared with the developer.
»» There is an option to de-identify data. VUNO Med-Chest X-ray only receives patient ID and study date.
Software Updates
Can vary depending on the business model. Please contact business@vuno.co for further information. There is no price differentiation for private and public sector.
Product Development Method
Supervised deep learning (CNN, RNN)
Training
100 000 adult chest X-rays
Reference Standard
Computed tomography (CT), human reader
Publications
»» Sung, J., Park, S., Lee, S.M. et al. Added Value of Deep Learning-based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study. Radiology. 2021;299(2):450-459. https://doi.org/10.1148/radiol.2021202818
»» Kim, YG., Cho, Y., Wu, CJ. et al. Short-term Reproducibility of Pulmonary Nodule and Mass Detection in Chest Radiographs: Comparison among Radiologists and Four Different Computer-Aided Detections with Convolutional Neural Net. Sci Rep. 2019;9:18738. https://doi.org/10.1038/s41598-019-55373-7
»» Park, S., Lee, S.M., Lee, K.H. et al. Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings. Eur Radiol. 2020;30:1359–1368. https://doi.org/10.1007/s00330-019-06532-x
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