AI 4 TB (Artificial Intelligence for Tuberculosis)

Dates: 2019-2021

Funded by: Department for International Development (DFID), UK

Project overview: Tuberculosis (TB) remains the world’s top infectious cause of death and public health crisis in Southern Africa. Those who work in South Africa’s goldmines suffer TB rates amongst the highest worldwide, as both silica dust and silicosis (itself a chronic lung disease) increase vulnerability to TB. This burden is borne by black miners from South Africa and neighbouring countries whose rights were historically denied under racist colonial practices. Screening for TB and silicosis is needed to provide the hundreds of thousands of afflicted workers and their families with health services and social benefits to which they are entitled as a result of class action lawsuits that have prompted improvements in compensation and social protection benefits, but there are insufficient trained health professionals to conduct this.

Artificial intelligence (AI) systems have been developed to detect TB in chest x-rays (CXRs), but have never been validated in a population with a high burden of silicosis, which can mimic and mask TB. Our pilot studies are promising; with more silicosis radiographs in AI training sets, and adding demographic and exposure data, we are on the verge of developing an accurate tool to help meet this challenge. We believe that if we validate and improve existing artificial intelligence-driven chest x-ray systems, and add demographic, exposure and other clinical data, then we will have a tool that is accurate, feasible and acceptable for greatly improving the efficiency of diagnosing tuberculosis and silicosis in a population with a high burden of both diseases.

Articles in MEDIUM reporting on project progress: 

AI4TB: Opportunities for improving systems to provide social benefits to those who incurred occupational lung disease   
March 9, 2021

AI4TB: Ground-truthing machine-learning innovations Sept. 16, 2020

AI4TB: Ground-truthing from machine-learning innovations April 9, 2020

AI4TB: Moving forward on technical and decision-making challenges December 11, 2019

AI4TB: Can artificial intelligence systems be used to detect tuberculosis and silicosis among ex-miners in Southern Africa? October 27, 2019


Kistnasamy B, Yassi A, Yu J, et al. Tackling injustices of occupational lung disease acquired in South African mines: Recent developments and ongoing challenges. Globalization and Health. 2018;14(60).
This article summarizes the legacy of unmet need to address the burden of TB as a result of gold mining in South Africa.

Young C, Barker S, Ehrlich R, Kistnasamy B, Yassi A. Computer-aided detection for tuberculosis and silicosis in chest radiographs of gold miners of South Africa. International Journal of TB and Lung Diseases. 2020;24:444-451.
This article focuses on analysis of the effectiveness of applying AI to diagnose TB from chest x-rays.


Yassi et al. presentation made to stakeholders, Improving efficiency of assessing (ex)miners for tuberculosis (TB) and silicosis: Innovations to promote social justice, on May 5, 2021 PowerPoint available here

and another presentation, Use of computer aided detection to support triage for efficiency at the MBOD, here

Other resources:
Dying for Gold – Award-winning documentary on the effects of gold mining in South Africa on black miners.
Trailer of movie:
If you are interested in a screening of the GHRP copy of this film, please contact GHRP with a request.