NYC Langone Health
May 30, 2023
An artificial intelligence (AI) computer program that processes MRI results 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 CT scans. The n
technology, researchers say, may help design new diagnostic tools to better understand subtle brain
injuries that accumulate over time.
Experts have long known about potential risks of concussion among young athletes, particularly for
those who play high-contact sports such as football, hockey, and soccer. Evidence is now mounting that
repeated head impacts, even if they at first appear mild, may add up over many years and lead to
cognitive loss. While advanced MRI identifies microscopic changes in brain structure that result from
head trauma, researchers say the scans produce vast amounts of data that are difficult to navigate.
Led by researchers in the Department of Radiology at NYU Grossman School of Medicine, the new study
showed for the first time that the new tool, using an AI technique called machine learning, could
accurately distinguish between the brains of male athletes who played contact sports like football versus
noncontact sports like track and field. The results linked repeated head impacts with tiny, structural
changes in the brains of contact-sport athletes who had not had a concussion diagnosis.
“Our findings uncover meaningful differences between the brains of athletes who play contact sports
compared to those who compete in noncontact sports,” said study senior author and neuroradiologist Yvonne W. Lui, MD. “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,” added Dr. Lui, a
professor and vice chair for research in the Department of Radiology.
Dr. Lui 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
The new study, which published online May 22 in The Neuroradiology Journal, involved hundreds of
brain images from 36 contact-sport college athletes (mostly football players) and 45 noncontact-sport
college athletes (mostly runners and baseball players). The work was meant to clearly link changes
detected by the AI tool in the brain scans of football players to head impacts. It builds on a previous
study that had identified brain-structure differences in football players, comparing those with and
without concussions to athletes who competed in noncontact sports.
For the investigation, the researchers analyzed MRI scans from 81 male athletes taken between 2016
through 2018, none of whom had a known diagnosis of concussion within that time period. Contact-
sport athletes played football, lacrosse, and soccer, while noncontact-sport athletes participated in
baseball, basketball, track and field, and cross-country.
As part of their analysis, the research team designed statistical techniques that gave their computer
program the ability to “learn” how to predict exposure to repeated head impacts using mathematical
models. These were based on data examples fed into them, with the program getting “smarter” as the
amount of training data grew.
The study team trained the program to identify unusual features in brain tissue and distinguish between
athletes with and without repeated exposure to head injuries based on these factors. They also ranked
how useful each feature was for detecting damage to help uncover which of the many MRI metrics
might contribute most to diagnoses.
Two metrics most accurately flagged structural changes that resulted from head injury, say the authors.
The first, mean diffusivity, measures how easily water can move through brain tissue and is often used
to spot strokes on MRI scans. The second, mean kurtosis, examines the complexity of brain-tissue
structure and can indicate changes in the parts of the brain involved in learning, memory, and emotions.
“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,” said study lead
author Junbo Chen, MS, a doctoral candidate at NYU Tandon School of Engineering. “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.”
Chen adds that the study team next plans to explore the use of their machine-learning technique for
examining head injury in female athletes.
Funding for the study was provided by National Institute of Health grants P41EB017183 and
26C63000NYUPG118117. Further funding was provided by Department of Defense grant
In addition to Dr. Lui and Chen, other NYU Langone Health and NYU researchers involved in the study
were Sohae Chung, PhD; Tianhao Li, MS; Els D. Fieremans, PhD; Dmitry Novikov, PhD; and Yao Wang,