An AI computer program that processes magnetic resonance imaging (MRI) can accurately identify changes in brain structure that result from repeated head injury, a new study in student athletes shows.
These variations have not been captured by other traditional medical images such as computerised tomography (CT) scans.
The new technology, researchers believe, may help design new diagnostic tools to better understand subtle brain injuries that accumulate over time.
Study lead author Junbo Chen, MS, a doctoral candidate at NYU Tandon School of Engineering
“Our results highlight the power of artificial intelligence to help us see things that we could not see before, particularly ‘invisible injuries’ that do not show up on conventional MRI scans.
“This method may provide an important diagnostic tool not only for concussion, but also for detecting the damage that stems from subtler and more frequent head impacts.”
Experts have long known about potential risks of concussion among young athletes, particularly for those who play high-contact sports such as football, ice hockey and American football.
Evidence is now mounting that repeated head impacts, even if they at first appear mild, may add up over many years, causing cognitive loss.
While advanced MRI identifies microscopic changes in brain structure that result from head trauma, scientists say the scans produce vast amounts of data that is difficult to navigate.
The new study showed for the first time that the new tool using machine learning could accurately distinguish between the brains of male athletes who played contact sports like football versus noncontact sports like athletics.
The results linked repeated head impacts with tiny, structural changes in the brains of contact sport athletes who had not been diagnosed with a concussion.
Study senior author and neuroradiologist Yvonne Lui, MD, a professor and vice chair for research in the Department of Radiology at NYU Langone Health, said:
“Our findings uncover meaningful differences between the brains of athletes who play contact sports compared to those who compete in noncontact sports.
“Since we expect these groups to have similar brain structure, these results suggest that there may be a risk in choosing one sport over another.
The researcher adds that beyond spotting potential damage, the machine-learning technique used in their investigation may also help experts to better understand the underlying mechanisms behind brain injury.
