How AI might help in diagnosing mild concussions

Whether it's from a sports injury, whiplash, or a bump to the head, many patients with mild concussion don't even realize their minor injury can, if untreated, cause lifelong severe health issues. Even if a patient goes to the ER with their injury, it's estimated that 50%–90% of concussion cases go without a formal diagnosis, putting them at risk of dangerous complications such as brain bleeds and cognitive impairment.

A new research collaboration between the USC Viterbi School of Engineering and the USC Leonard Davis School of Gerontology has harnessed a powerful machine learning model to predict concussion status in patients. The work, led by Benjamin Hacker (B.S. '24), has now been published in the Journal of Neurotrauma.

A concussion is a form of traumatic brain injury that can cause temporary changes to the brain's function. Hacker said that current clinical practice for concussion diagnosis often relies on basic cognitive tests such as the Glasgow Coma Scale, a tool used to assess a patient's level of consciousness, responsiveness and memory.

Yet, many mild concussion patients never lose consciousness and may not present with the traditional cognitive symptoms that would make them easy to diagnose. Hacker said that this existing testing was not sensitive enough to detect many milder cases.

"We saw an opportunity to fit in that space between 'not a concussion at all' and a concussion that's severe enough that it is consistently being detected," said Hacker, who authored the paper as a USC Viterbi undergraduate and is now a master's student in the Mork Family Department of Chemical Engineering and Materials Science.

Read the full article here.