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Last Updated:
17 April 2020
T-Xnet is intended to mark regions of a chest X-ray with specific abnormalities associated with tuberculosis (TB) or pneumonia and alert radiologists and clinicians to these regions during image interpretation.
Certification
Pending
Development Stage
Validation
Deployment
Offline only
Intended Age Group
18+ 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
Currently not on the market
Input
»» Can be used to read images from any kind of chest X-ray machine (vendor neutral)
»» Chest X-ray image format: JPEG, PNG, DICOM, TIFF
»» Chest X-ray type: posterior-anterior chest X-ray, anterior-posterior chest X-ray, lateral chest X-ray, portable
»» Other requirements: none
Output
Structured report including:
»» Heat map,
»» Probability score for TB,
»» Probability score for the following pulmonary abnormalities: atelectasis, cavity, mass, nodule, pleural effusion, pneumothorax
1/1
Hardware
A computer with minimum 2 GB RAM and CPU with at least 2.3 GHz (small stick computer is sufficient).
Validation
Optional: Fine tuning may be useful in certain scenarios. For this, a dataset with a particular machine will need to be prepared.
Hardware
A computer with minimum 2 GB RAM and CPU with at least 2.3 GHz (small stick computer is sufficient).
Server
Minimum 2 GB RAM and CPU greater than or equal to 2.3 GHz
Integration
It is possible to integrate the product with the client’s legacy picture archiving and communication system (PACS).
Software
No external dependencies
Processing Time
Maximum 10 seconds
Data Sharing & Privacy
»» Data are not shared with the manufacturer
»» There is an option to de-identify data
Price
»» Flexible pricing models are available.
»» Please contact company for quote: Rajarajeshwari K. at kraji@artelus.com
Software Updates
»» Software is updated every 6 months
»» Two options for upgrades: if there is no internet connection available, the manufacturer will ship a replacement device to the client; or they will require a momentary internet connection to run an online upgrade
»» Extra costs: none
Product Development Method
Supervised deep learning (CNN, RNN)
Training
The product was trained on 120 000 chest X-rays from USA and China
Reference Standard
Human reader
Publications
Peer-reviewed publications are not yet available.
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