Last Updated:
15th May 2023
Determining the activity of pulmonary tuberculosis (TB) on chest radiographs is difficult. Medical IP's TiSepX TB deep learning model can help determine TB activity and differentiate active TB from healed TB on chest radiographs comparable to expert clinicians. The model-driven activity score reflects bacilli burden and treatment response. Medical IP also has other products available that allow for the detection of COVID-19 or pneumonia.
Certification
Pending (Korea MFDS)
Development Stage
On the Market
Deployment
Online & Offline
Intended Age Group
20+ years
Target Setting
Primary health centres, general hospital (above primary level), teleradiology companies, government/public sector, e.g. national TB programme, private sector, high-TB burden countries
Current Market
Republic of Korea, lower-resource countries with Official Development Assistance (ODA) programmes.
Input
Chest X-ray image format: JPEG, PNG, DICOM, Neuroimaging Informatics Technology Initiative files (NII), bitmap (BMP)
Chest X-ray type: posterior-anterior chest X-ray, anterior-posterior chest X-ray, portable
Output
Heat map, Probability score for identifying active TB from healed TB / Score trajectory during anti-TB treatment
Lung findings identified by artificial intelligence:
Algorithm does not specify abnormalities
Lung findings included in TB score:
abscess, airfluid level, blunted costophrenic angle, cavity, chest wall invasion/destruction, consolidation, interstitial markings, loculated pleural effusion, lymphadenopathy, mass, nodule, opacity, pleural effusion, prominence in hilar region, probability score for identifying active TB from healed TB
Disease scores identified (detection of other diseases on chest X-ray beyond TB could come with additional cost):
COVID-19, pneumonia
Format: PNG
Hardware
Online: stable internet access and internet browsers (i.e. Chrome, Safari) are enough to execute the program for online use.
Offline: memory supporting Compute Unified Device Architecture (CUDA)
Hardware Requirements: TiSepX Lung / TiSepX COVID / TiSepX TB -> 8GB GPU
Server
Main server is located in Republic of Korea, a local server can be set up if required.
Hardware
Online: stable internet access and internet browsers (i.e. Chrome, Safari) are enough to execute the program for online use.
Offline: memory supporting Compute Unified Device Architecture (CUDA)
Hardware Requirements: TiSepX Lung / TiSepX COVID / TiSepX TB -> 8GB GPU
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) for on-premise type only.
Software
Software Requirements: NVIDIA CUDA required
Processing Time
10-20 seconds (depending on the X-ray image resolution and size)
Data Sharing & Privacy
Server location (for online product):
main server is located in South Korea, a local server can be set up if required.
Data are not shared with the developer.
There is an option to de-identify data. The TiSepX TB server anonymizes all personal information before uploading original images to the server.
Price
Volume-based pricing models are available. Please contact company for quote: sales@medicalip.com
Software Updates
As soon as the module is developed, an update is made after the test. Regular updates happen twice a year, but irregular updates might happen if abnormality function is discovered.
Please contact company for information: tech@medicalip.com
Product Development Method
Unsupervised deep learning
Training
9836 adult chest X-rays
Reference Standard
Human reader, computed tomography (CT)
Publications
Lee, S., Yim, J.J., Kwak, N. et al. Deep Learning to Determine the Activity of Pulmonary Tuberculosis on Chest Radiographs. Radiology. 2021;301(2):435-442. https://doi.org/10.1148/radiol.2021210063
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