The study involved analyzing breath samples from 31 silicosis patients and 60 healthy individuals, aiming to differentiate between affected and unaffected subjects.

This rapid, non-invasive breath test utilizes mass spectrometry and artificial intelligence to analyze breath samples, providing results in under five minutes, making it suitable for routine screening.

The project received funding from the iCare Dust Diseases Board through a Discovery and Innovation grant, facilitating this important research.

A groundbreaking study published in the Journal of Breath Research has introduced an AI-powered breath test capable of detecting silicosis within minutes, boasting over 90% accuracy and surpassing traditional lung function tests.

Silicosis, an incurable yet preventable lung disease caused by silica dust exposure, is increasingly affecting construction workers and others in high-risk industries, underscoring the need for effective early diagnosis.

Future plans include refining the AI model, validating the test with larger and more diverse worker populations, and exploring on-site testing feasibility for routine screening.

Despite its promising potential, further validation with larger sample sizes is necessary before the breath test can be implemented as a standard screening tool.

The new technology presents a practical option for routine screening of workers exposed to silica dust, potentially revolutionizing workplace health monitoring.

Current diagnostic methods, such as X-rays and CT scans, only identify silicosis at later stages, complicating early intervention efforts and often revealing irreversible damage.

Participants in the study breathe into a bag, and their breath is analyzed using mass spectrometry and AI to identify specific organic compounds related to silicosis.

The study was led by Professor William Alexander Donald and Conjoint Professor Deborah Yates, highlighting the collaborative effort behind this innovative approach.

Patients with advanced silicosis experience severe breathing difficulties and may require lung transplants, emphasizing the critical importance of early diagnosis for effective intervention.