A new multi-year project involving several American universities and national laboratories aims to use supercomputing resources and artificial intelligence (AI) to enable a precision medicine approach for treating traumatic brain injury (TBI).
The participating institutions include the Department of Energy’s (DOE) Lawrence Livermore (LLNL), Lawrence Berkeley (LBNL) and Argonne (ANL) national laboratories, in collaboration with the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) consortium led by the University of California, San Francisco (UCSF) and involving other leading universities across the US.
Funded primarily by the National Institutes of Health’s National Institute of Neurological Disorders and Stroke (NINDS) DOE scientists will analyse some of the largest and most complex TBI patient data sets collected, including advanced computed tomography (CT) and magnetic resonance imaging (MRI), proteomic and genomic biomarkers and clinical outcomes. To do this they will use artificial intelligence based technologies and supercomputing resources.
NINDS scientific program officer Patirck Bellgowan said: “Consistent with the National Research Action Plan for traumatic brain injury, this is a powerful example of leveraging the strengths and research investments of multiple federal agencies to accelerate discovery and enhance care for those suffering from TBI.”
UCSF’s contact principal investigator and professor of neurosurgery Geoff Manley said his team hopes the collaboration will act as a driving force for successful analytic and technological advancements and help advance missions in medicine initiatives.
He said: “By harnessing subject matter expertise across the participating universities and labs, and using next-generation artificial intelligence-based tool development for precision diagnostics, we are optimistic that we will be able to create clinically actionable information to guide personalised treatment for TBI patients.”
LLNL hope to use the problem of TBI to open new frontiers in AI and data science on high-performance computing and to develop new methods of diagnostics for DOE missions. LLNL director of innovation Jason Paragas said: “Making sense of the brain at 100 billion neurons and 10,000 connections at the ends of each neuron is a calculation that is at the frontiers of our supercomputers and a job only the machines at the national laboratories can do.”
The project began in March of this year but is still in its initial stages. The DOE and USCF teams will continue working on the neural model, while LBNL and ANL scientists will focus on data analytics, including genetic information.