MSGID: 1:317/3 6476cdbd
PID: hpt/lnx 1.9.0-cur 2019-01-08
TID: hpt/lnx 1.9.0-cur 2019-01-08
 New tool may help spot 'invisible' brain damage in college athletes


  Date:
      May 30, 2023
  Source:
      NYU Langone Health / NYU Grossman School of Medicine
  Summary:
      An artificial intelligence 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 computerized tomography
      (CT) scans.

      The new technology, researchers say, may help design new diagnostic
      tools to better understand subtle brain injuries that accumulate
      over time.


      Facebook Twitter Pinterest LinkedIN Email

==========================================================================
FULL STORY
==========================================================================
An artificial intelligence 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 computerized tomography (CT) scans. The new
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 is 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 been diagnosed with a concussion.

"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
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," adds Lui, a professor and vice chair for research in the
Department of Radiology at NYU Langone Health.

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 injury.

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 C63000NYUPG118117. Further funding was provided
by Department of Defense grant W81XWH2010699.

In addition to Lui and Chen, other NYU researchers involved in the study
were Sohae Chung, PhD; Tianhao Li, MS; Els Fieremans, PhD; Dmitry Novikov,
PhD; and Yao Wang, PhD.

    * RELATED_TOPICS
          o Mind_&_Brain
                # Brain_Injury # Intelligence # Brain-Computer_Interfaces
                # Disorders_and_Syndromes
          o Computers_&_Math
                # Neural_Interfaces # Computer_Modeling # Communications
                # Hacking
    * RELATED_TERMS
          o Magnetic_resonance_imaging o Functional_neuroimaging
          o Headache o Traumatic_brain_injury o Brain_damage o
          Computer_vision o Head_injury o Neuropsychology

==========================================================================
Story Source: Materials provided by
NYU_Langone_Health_/_NYU_Grossman_School_of_Medicine.

Note: Content may be edited for style and length.


==========================================================================
Journal Reference:
   1. Junbo Chen, Sohae Chung, Tianhao Li, Els Fieremans, Dmitry
   S. Novikov,
      Yao Wang, Yvonne W. Lui. Identifying relevant diffusion MRI
      microstructure biomarkers relating to exposure to repeated head
      impacts in contact sport athletes. The Neuroradiology Journal,
      2023; 197140092311773 DOI: 10.1177/19714009231177396
==========================================================================

Link to news story:
https://www.sciencedaily.com/releases/2023/05/230530125434.htm

--- up 1 year, 13 weeks, 1 day, 10 hours, 50 minutes
 * Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)
SEEN-BY: 15/0 106/201 114/705 123/120 153/7715 218/700 226/30 227/114
SEEN-BY: 229/110 112 113 307 317 400 426 428 470 664 700 291/111 292/854
SEEN-BY: 298/25 305/3 317/3 320/219 396/45
PATH: 317/3 229/426