Optimizing computational modeling of traumatic brain injury with machine learning: biomechanics and beyond

Traumatic brain injury (TBI) has become a global health hazard. If undetected, the brain damage of TBI can accumulate, calling for better TBI modeling and warning systems. TBI modeling involves three stages: head impact kinematics, brain deformation, and injuries. This project will leverage machine learning and head impact data to optimize the TBI computational modeling: the measurement precision of head kinematics will be improved and the fast, accurate and generalizable brain deformation modeling will be done with deep learning. Furthermore, this project will leverage animal experiments to investigate the link between brain deformation and the TBI pathologies for injury-predictive models.

Project Details

Funding Type:

SIGF - Graduate Fellowship

Award Year:


Lead Researcher(s):

Team Members:

David Benjamin Camarillo (Primary Advisor, Bioengineering)
Olivier Michel Simonne Gevaert (Co-Advisor, Biomedical Data Science)