From coal miners and construction crews to engineered stone countertop fabricators and foundry workers, more than 2.2 million U.S. workers inhale fine particles of rock, sand or coal every day on the job. After years of exposure, trapped dust can cause thick scar tissue to build up inside the lungs leading to long-term lung damage commonly known as black lung or silicosis.
Early diagnosis helps organizations know how to care for their employees, so regular chest X-rays are required. Because the particles that get trapped in lungs are very small, the X-rays are reviewed by specially trained physicians known as B readers. In the U.S. right now, there are just 200 B readers, which is a massive shortage.
To offer workers faster protection, researchers from Michigan State University organized a specialized dataset of U.S. worker scans to train an artificial intelligence, or AI, program. Funded by a $600,000 grant from the National Institute for Occupational Safety and Health, or NIOSH, this is the first tool of its kind built specifically using images from U.S. workers. MSU’s study was published in Occupational and Environmental Medicine.
“For workers in dusty environments, time is everything,” said Kenneth Rosenman, chief of the Division of Occupational and Environmental Medicine within the MSU College of Human Medicine and a certified B reader. “If a worker’s lung disease goes undetected because of screening delays, they may remain in a high-dust environment, and their lungs will continue to scar. By giving doctors an objective second opinion, this tool helps us catch disease at the early stage, allowing employers to remove workers from dangerous dust and reduce the likelihood of the workers’ lung disease from progressing.”