Traumatic Brain Injuries (TBIs) impact approximately 2.8 million Americans annually, in which treatment pathways are determined by severity classification. Current TBI case management leads to high mistriage rates and severity misclassifications due to subjectivity of current diagnostic methods (i.e. GCS scale). Time and resources are wasted, cases are missed, and patients are rerouted. TBIs are highly time sensitive; even minutes or hours of delay connecting a patient to the right course of care may result in worse clinical outcomes for the patients which leads to higher overall mortality. The group most disproportionately affected by misdiagnosis is patients with moderate TBIs. There is currently no definitive mechanism for first responders to determine the level of injury, making it difficult to properly categorize moderate TBI severity, leading triage to be delayed until arrival in hospital. To best improve clinical outcomes for these patients, the time from accident to appropriate triage and thus treatment must be decreased, allowing improved resource allocation efficiency. We aim to do just that through our technology, proposing a novel device that outputs the severity of a patient’s TBI and is entirely usable in a point-of-care ambulance setting, specifically targeting moderate TBI classification. Within the workflow, our system is capable of returning a TBI severity score within 15 minutes, allowing ambulance routes to change and providing more rapid treatment based on the test results. Our technology is the first comprehensive system for biomarker-based TBI detection capable of being used in an ambulance setting with first responder training.

Team Members

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Project Mentors, Sponsors, and Partners

• Dr. Elizabeth Logsdon (Faculty mentor)

• Dr. William Anderson (Clinical mentor)

• Dr. Tza-Huei Wang (Committee member)

• Dr. Hoan Ngo (Committee member)

• Dr. Matthew Levy (Committee member)

• Aseem Jain (Teaching assistant)

Course Faculty

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Project Links

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Additional Project Information