Ali A. Rostam-Alilou

Title: Development of machine learning tools to predict pathological sequelae of traumatic brain injury

Supervision Team: Nicholas Dunne, DCU / Caitríona Lally, TCD / David MacManus, DCU / David Loane, TCD

Description: Traumatic brain injuries (TBI) are one of the leading causes of death and disability worldwide. Currently, there is a huge gap in diagnostic and prognostic technologies for TBI likely due to its multiple biological and biomechanical aspects which can cause different neurological pathologies, impairments, and deficits in people. This multifaceted nature of TBI makes it difficult to predict the pathological outcomes using existing technologies. Indeed, it remains difficult to not only diagnose certain TBIs e.g., concussion, but it is also difficult to determine an accurate prognosis. Therefore, the aim of this research is to develop state-of-the-art computational tools to predict TBI pathologies and provide accurate diagnoses and prognoses. This will be achieved by using TBI pathology data (e.g., MRI) with state-of-the-art computer models of the brain to develop novel computational tools utilising machine learning to determine accurate diagnoses and prognoses of TBI from head impacts e.g., sports-related head impacts, falls.