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Pratik Mukherjee, MD, PhD, Professor of Radiology and Biomedical Imaging, Bioengineering, UC San Francisco
(L to R, Top Row: [UCSF] Pratik Mukherjee, MD, PhD; Esther Yuh, MD, PhD; J. Claude Hemphill, MD; Nerissa Ko, MD; Bottom Row: [UCSF] Geoffrey T. Manley, MD, PhD; [UCB] Jitendra Malik, Phd; [Stanford] Jamshid Ghajar, MD, PhD; [Community Regional Medical Center at Fresno] Arash Afshinnik, MD]
Every 28 seconds, an American suffers a catastrophic neurologic emergency, most commonly stroke or traumatic brain injury (TBI). Neurologic emergencies affect 15 million U.S. adults and children annually at a cost of $115 billion, which is 7% of total U.S. healthcare spending per year.
Since the brain is susceptible to irreversible injury within minutes, immediate diagnosis and treatment are essential. Computed tomography (CT) scanning is currently the only type of imaging used worldwide to diagnose neurologic emergencies. Immediate diagnosis aided by rapid automated evaluation of head CT could greatly improve care in situations where minutes count.
This project will apply state-of-the-art artificial intelligence (AI) technology to automatically recognize life-threatening findings on emergency head CT scans in patients suspected of having TBI, stroke or bleeding due to ruptured brain aneurysms and aims to assist physicians to make a quicker diagnosis.
Another important advance enabled by this technology is the ability to catalogue clinically significant “digital markers” that are recognizable across scans, which will facilitate future precision medicine research by combining data from quantitative image analyses with other types of data.
The team will implement this AI system in the “cloud” so that its use does not remain limited to advanced hospital settings and CT scans can be uploaded for analysis from anywhere in the world.