Researchers from Sydney have achieved a significant advancement in the detection of silicosis, a lung disease triggered by inhaling fine silica dust. This breakthrough comes with the introduction of a non-invasive diagnostic tool powered by artificial intelligence, which analyses a person’s breath to identify early indicators of the disease within minutes. This innovative approach holds the potential to be life-saving for vulnerable workers in high-risk industries.
Professor William Alexander Donald, the lead researcher, explained that the AI model successfully differentiates the breath profiles of individuals afflicted with silicosis from those who are healthy, demonstrating impressive accuracy. Silicosis has been notably linked to the use of engineered stone materials—commonly employed in kitchen benches—which gained popularity during the construction boom of the early 2000s. The high silica content in these materials has led to increased rates of lung disease, prompting Australia to enact a ban on engineered stone last year.
Former stonemason Kyle Goodwin shared his personal experience, expressing that had he had access to such early detection tools, he might have left the industry much earlier, avoiding the health complications associated with silicosis.
Despite the ban on engineered stone, new cases of silicosis persist in other high-risk sectors, emphasising the urgent need for effective diagnostic solutions. Professor Donald highlighted that their research indicates the AI model is capable of accurately identifying silicosis through breath analysis, which not only aids in early detection but could also serve as a practical method for large-scale screening and intervention in workplaces.
Currently, a trial is being conducted at mining sites in the Hunter Valley, with aspirations for the diagnostic tool to be implemented in the workplace within the next couple of years. This initiative represents a vital step forward in protecting workers from the harmful effects of silica dust, aiming to reduce the incidence of silicosis through prompt identification and intervention.