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Product:

TBXNet++

V4

Company HQ: 

H-8/4 Islamabad, Paksitan

Certification
Download Product Profile

Last Updated:

25 January 2025

TBXNet++ is an advanced AI-powered software for efficient tuberculosis (TB) detection in chest X-rays. It classifies cases as either normal (no TB) or abnormal (presence of TB) and provides a confidence score in percentage for each prediction. The software includes heatmaps to visually highlight affected lung areas, aiding clinicians in diagnosis. Designed for adaptability, TBXNet++ operates seamlessly in online and offline environments, making it suitable for various healthcare settingsy. With a streamlined focus on TB detection, it supports high-throughput screening and timely clinical decision-making. By integrating TBXNet++, healthcare providers can improve diagnostic accuracy, enhance workflow efficiency, and contribute effectively to TB control efforts. Demo and support services are available.

Certification

Pending

Development Stage

On the Market

Deployment

Online & Offline

Intended Age Group

16+ 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

Pakistan

Input

Chest X-ray image format: JPG, PNG
Chest X-ray type: Posterior-anterior chest X-ray, Anterior-posterior chest X-ray

Output

Output includes:

  • Heatmap

  • Dichotomous output indicating whether TB is likely present or absent

  • Probability score for TB

  • Probability score for each abnormality

  • Location of each abnormality

Results are displayed in a tabular format, with an option for clients to export detailed PDF reports for convenient sharing and record-keeping.


Lung abnormalities included in the TB Score: Cavity, Consolidation, Opacity


Additional findings reported by the product:Cavity, Consolidation, Fibrosis, Nodule, Opacity, Prominence in hilar region 

Hardware

The product is available online at out website and no hardware requirement for using.
For on-premise settings, TBXNET++ can either be deployed on a computer or laptop with minimum 8 GB memory. Please contact via email for more information. ilm@szabist-isb.edu.pk

Server

TBXNET++ can be deployed on cloud servers built by cloud hosting partners with the highest standards for privacy and data security. We also deploy on-premise servers that meet required specifications of 16GB memory or above and 8GB graphic card. Internet bandwidth of 5Mbps or above.

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Hardware

The product is available online at out website and no hardware requirement for using.
For on-premise settings, TBXNET++ can either be deployed on a computer or laptop with minimum 8 GB memory. Please contact via email for more information. ilm@szabist-isb.edu.pk

Integration with X-ray Systems

Integration with PACS and Legacy Systems

It can be integrated with third party X-ray system. An API can take the x-ray system file from the software to our system for prediction.

Please contact via email for details. ilm@szabist-isb.edu.pk

Software

No special software is required incase of online usage,For Offline Usage Windows 10, minimum 8 GB RAM

Processing Time

Around 0.06 - 0.24 seconds per X-ray

Data Sharing & Privacy

TBXNet++ ensures complete data privacy —no patient information is stored. However, X-ray images are stored on our secure web server. X-ray images are never shared without consent of the patients.

Price

Pricing is flexible, with multiple subsciptions options available for customers. Both one time susbscription or per x-ray image scan option is available. Please contact via email or call for detailed information or quotation.

Software Updates

Every 6 months

Product Development Method

Deep Learning, CNN, Transformers

Training

The product was trained on 17,000 chest X-rays.

Reference Standard

Human reader (radiologists specializing in TB)

Publications

1. Iqbal, Ahmed, Muhammad Usman, and Zohair Ahmed. "Tuberculosis chest X-ray detection using CNN-based hybrid segmentation and classification approach." Biomedical Signal Processing and Control 84 (2023): 104667. https://doi.org/10.1016/j.bspc.2023.104667


2. Iqbal, Ahmed, Muhammad Usman, and Zohair Ahmed. "An efficient deep learning-based framework for tuberculosis detection using chest X-ray images." Tuberculosis 136 (2022): 102234.

https://doi.org/10.1016/j.tube.2022.102234


3.Asad, Muhammad, Azhar Mahmood, and Muhammad Usman. "A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries." Tuberculosis 123 (2020): 101944.

https://doi.org/10.1016/j.tube.2020.101944

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