THE MOST EXCITING ACCOMPLISHMENT HAS BEEN SUCCESSFULLY BRINGING TOGETHER PHYSICIANS, BIOMEDICAL SCIENTISTS, AND DATA SCIENTISTS WITH DIFFERENT EXPERTISE AND PERSPECTIVES TO WORK ON A COMMON GOAL OF ACCELERATING TBI PRECISION MEDICINE.
In short, the problem isn’t a lack of data—it’s making sense of it all. That’s where machine learning enters the picture. The UC research team developed advanced AI-driven image processing pipelines that extract crucial features from CT scans, offering insights that go beyond what the human eye can detect. Using high-performance computing infrastructure at UC Berkeley and the National Energy Research Supercomputing Center, these machine learning models are designed to provide clinicians with precise, quantitative assessments of TBI severity, injury patterns and likely outcomes.
Image courtesy of UC Berkeley
Collaboration at the Cutting Edge Led by Geoffrey Manley, M.D., Ph.D., chief of neurosurgery at Zuckerberg San Francisco General Hospital and professor at UCSF, and co-principal investigator Adam Ferguson, M.S., Ph.D., the project has leveraged a wealth of resources, including UCSF's Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) dataset. This TRACK-TBI study provided a comprehensive database of clinical information, neuroimaging, blood biomarkers and patient outcomes. By integrating this dataset with a clinical TBI registry at UC Davis, the team was able to cross-validate findings and refine their machine learning models. “The most exciting accomplishment has been successfully bringing together physicians, biomedical scientists, and data scientists with different expertise and perspectives to work on a common goal of accelerating TBI precision medicine,” said Manley. “We gathered massive amounts of clinical imaging data, curated them at UCSF, and prototyped a machine learning pipeline, which is now being deployed at scale in cloud-based supercomputing infrastructure at UC Berkeley.”
ucnoyce.org 25
Made with FlippingBook Annual report maker