AI & TBI Bringing artificial intelligence to the forefront of traumatic brain injury research
Geoffrey Manley, M.D., Ph.D. Professor Neurosurgery UC San Francisco
By Sarah Colwell
A child’s sledding accident. A bad football tackle. A wartime explosion near a service member. These are just a few of the ways traumatic brain injuries can occur, upending a person’s life in an instant. Every year, nearly 2.8 million people in the U.S. suffer a traumatic brain injury (TBI), a condition that can have life- altering consequences. From emergency department visits to long-term rehabilitation, the challenge of diagnosing and treating TBI remains complex and, at times, imprecise. The reason being, our current understanding, ability to accurately diagnose, and treatment of this condition remains, largely, understudied. With support from the UC Noyce Initiative, a multidisciplinary team of researchers from three University of California campuses—Berkeley, Davis, and San Francisco— are developing cutting-edge, machine learning that is paving the way for a new era in TBI diagnosis and prognosis. Together, the team of computational sciences and physician- scientists are working to tackle a crucial problem: how to harness vast amounts of medical data, particularly CT scans, to improve patient outcomes. From Data Overload to Actionable Insights TBI diagnosis often begins with a head CT scan of the brain, yet these scans are typically classified in a binary fashion: positive (indicating hemorrhage) or negative (showing no visible trauma). However, these images contain a wealth of underutilized information that could significantly refine diagnoses and predict patient trajectories.
Lara Zimmerman, M.D. Assistant Professor Neurological Surgery and Neurology UC Davis
Kristofer Bouchard, Ph.D. Adjunct Professor Helen Willis Neuroscience Institute UC Berkeley
Adam Ferguson, Ph.D. Professor Neurological Surgery UC San Francisco COMPUTATIONAL HEALTH
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