In Brief

The Challenge

Traumatic brain injury (TBI) is a major cause of death and disability worldwide. It is also called ‘a silent epidemic’ as patients may not initially show signs of brain injury. Broadly speaking it is caused by an external force that injures the brain by jolting or hitting the head. The current computational models used to predict brain deformation for trauma are either low-fidelity or not suitable to be deployed for real-time predictions for diagnostic purposes. Current practice requires on-field consciousness-based subjective assessments.  

Mild TBIs can lead to degenerative brain disorders (like dementia), with all of the impacts on quality of life and economic potential that result. 

The Solution

The solution to this challenge is to develop a ‘digital twin’, i.e., a machine learning model trained to predict brain deformation using head motion data, and to make real-time predictions. It will be able to predict with varying degrees of fidelity. It could assist the players in contact sports (like rugby) to make instant assessment on concussions. We will first develop the optimal artificial neural network to clarify the measurements involved. 

The Team

  • Team Lead: Dr Bharat Bhushan Tripathi, University of Galway  
  • Team Co-Lead: Professor Michael Gilchrist, University College Dublin

Societal Impact Champion

  • Dr Nicol van Dyk, Irish Rugby Football Union