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Version 1.2

Company HQ: 

Hyderabad, India

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Last Updated:

28 September 2021

ImagInfer is a deep learning-based inferring solution for tuberculosis (TB) detection and can be further trained with data from the client to recognize other conditions, such as pneumonia and COVID-19 on chest X-rays. This solution can have multiple use cases. Where a radiologist is not available (for instance in rural areas), inference can happen with high accuracy. In big hospitals in developed countries where radiologists may have limited time, the system can reprioritize X-rays for reporting instead of “first in first out”.
New models for new conditions can also be created with the training data provided.


Pending. Undergoing validation at State TB Offices in India. CE certification planned

Development Stage



Hybrid (offline analysis of the image but online on demand synchronization with legacy systems or cloud storage)

Intended Age Group

8+ 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

India, Africa


»» Chest X-ray image format: JPEG, PNG, DICOM, Tiff, PDF

»» Chest X-ray type: posterior-anterior chest X-ray, anterior-posterior chest X-ray, lateral chest X-ray, and any X-rays as long as they are used for training the model


»» Dichotomous output indicating whether each abnormality  is present or absent

»» Dichotomous output only indicating whether TB is likely present or likely absent

»» No score is provided



This is dependent on the volume concurrency and expected speed. In general, a 24 GPU machine is required for training and a 12 GPU machine is required for inference.


No validation is required for the X-ray machine. The quality of the output from the X-ray machine should be good and if the model is trained using the output from the same X-ray machine, the accuracy will be very high.


This is dependent on the volume concurrency and expected speed. In general, a 24 GPU machine is required for training and a 12 GPU machine is required for inference.


Hybrid deployment model, which can be set up anywhere. On premise deployment can be done as well as a cloud deployment.


Integrations and other customizations may be possible on request. An appropriate quote will be provided.



Processing Time

6 seconds

Data Sharing & Privacy

»» Server location (for online product): A hybrid deployment model, can be set up anywhere. On-premise deployment can be done, as well as a cloud deployment.

»» Data are not shared with the manufacturer

»» There is no option to deidentify data.

»» The image data and not the corresponding metadata present in the file is used. For example, if a DICOM file is given as input, then only the image data from that and not the metadata, that constitutes the information about the patient, is utilized.


Please contact for information.

Software Updates

»» Depends on any new features added. In general, a 6-month window is kept for upgrades.

»» Updates will normally be free if the client is under an annual maintenance contract. If some completely new modules are added, then the pricing will be modified accordingly as per the module.

Product Development Method

Supervised deep learning (CNN, RNN)


670 adult chest X-rays to train and create a model for any condition. Public datasets from China.

Reference Standard

Mostly human readers


Peer-reviewed publications are not yet available.

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