Last Updated:
2 September 2021
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
Validation
If the product is integrated with the machine (works as a part of the machine), a separate validation is normally required. If the product works as a separate software, the X-ray machine does not require validation before use. The product works well with X-ray images using most digital and computed radiographic machines but cannot be assured if X-ray is acquired using film radiography.
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.
Integration
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
This website works best with browsers other than Internet Explorer.