Product:

TiSepX TB

Version 1

Company HQ: 

Seoul, Republic of Korea

Certification
Download Product Profile

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

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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

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