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Last Updated:
15th May 2023
This is a deep learning-based computer-aided detection model applicable to chest X-ray interpretation and intended to rule out tuberculosis (TB) in low prevalence populations such as contacts of TB index patients. This would be particularly useful to speed up the cascade of care for populations eligible for TB preventive treatment.
Certification
Not available
Development Stage
Under Development
Deployment
Online and device-centric workflows
Intended Age Group
18+ years
Target Setting
Primary health centers, government/public sector, e.g. national TB programme
Current Market
Planned market: Brazil, Russia, India, China, and South Africa, other South American countries
Input
JPEG, DICOM; currently using JPEG but can easily support DICOM
Posterior-anterior or anterior-posterior chest X-ray
Output
Abnormality score for TB and for each covered finding. The location of abnormalities by side (left and right) and thirds (first, second or third).
1/1
Hardware
At the current stage, a notebook computer is necessary.
Server
NIC Card: 2 NIC Cards - 10/100/1000Mbps
Hardware
At the current stage, a notebook computer is necessary.
Integration with X-ray Systems
Integration with PACS and Legacy Systems
Not possible to integrate the current version with the client's legacy picture archiving and communication system (PACS).
Software
To be determined
Processing Time
To be determined
Data Sharing & Privacy
To be determined
Price
Open-source (free) software and artificial intelligence models distributed under the Affero GNU Public License version 3 (AGPLv3)
Software Updates
To be determined
Product Development Method
Training
Reference Standard
Human reader
Publications
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