The conference is being held under the theme Dust and infection free lungs: harnessing artificial intelligence for TB and Silicosis is part of its efforts to eradicate the killer diseases.

A three-day conference that ends tomorrow is considering the adoption of Artificial Intelligence (AI) to expedite the screening and diagnosis of tuberculosis (TB) and silicosis in South Africa.

The Department of Health has hailed the conference as a pivotal platform for robust engagement, knowledge sharing, and innovation to accelerate progress towards ending TB. 

It follows on the backdrop of recommendations from the World Health Organization for member states to use computer-aided detection software (CAD) to interpret chest X-rays when screening and triaging for tuberculosis.

TB experts and key stakeholders in attendance have prioritised discussing how AI can be used to enhance the diagnosis of both TB and silicosis in the country to address the current backlogs, especially amongst people who contracted occupational health diseases while working in the mines.

Although South Africa has made significant strides in the fight against TB since 2010, as evidenced by the slow but steady decline in TB incidence and mortality, the incidence of TB in South Africa, estimated by the World Health Organisation to be 468 per 100,000 of the population in 2022, remains stubbornly high.

Presently the Eastern Cape, KwaZulu-Natal and Western Cape have the highest incidence rates in the country, with reported rates of 692, 685 and 681 per 100 000, respectively.

The Department of Health underlined that this conference being held under the theme Dust and infection free lungs: harnessing artificial intelligence for TB and Silicosis is part of its efforts to eradicate the killer diseases.

“This is an opportune time to help the country gain awareness of the Computer Aided Detection diagnostic tools to assist towards the End TB Goal by 2035. Chest radiography remains an essential tool for screening and evaluating diseases of the Chest,” the Department said.

According to the Department, the need to adopt AI has been necessitated by the shortcomings that have been picked up in the current radiological methods, including chest X-rays that have been integral in diagnosing TB and silicosis among mineworkers.

“These methods have limitations, especially in differentiating between TB and silicosis, due to their similar radiological presentations as well as silico-tuberculosis,” said the Department.