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Version 1

Company HQ: 

Bangalore, India


Last Updated:

15th May 2023

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.



Development Stage

Under Development


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


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


Structured report including: 

Heat map, 

Probability score for TB, 

Probability score for the following pulmonary abnormalities: atelectasis, cavity, mass, nodule, pleural effusion, pneumothorax


A computer with minimum 2 GB RAM and CPU with at least 2.3 GHz (small stick computer is sufficient).


Minimum 2 GB RAM and CPU greater than or equal to 2.3 GHz



A computer with minimum 2 GB RAM and CPU with at least 2.3 GHz (small stick computer is sufficient).

Integration with X-ray Systems

Integration with PACS and Legacy Systems

It is possible to integrate the product with the client’s legacy picture archiving and communication system (PACS).


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


Flexible pricing models are available.
Please contact company for quote: Rajarajeshwari K. at

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)


The product was trained on 120 000 chest X-rays from USA and China

Reference Standard

Human reader


Peer-reviewed publications are not yet available.

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