Abstract #3014

Analysis of automatically extracted structural features to describe traumatic brain injury severity

Marianna La Rocca¹, Giuseppe Barisano¹, Ryan Cabeen¹, Paul Vespa², Arthur W. Toga¹, and Dominique Duncan¹

¹Laboratory of Neuro Imaging, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Keck School of Medicine, Los Angeles, CA, United States, ²Departments of Neurology and Neurosurgery, David Geffen School of Medicine, The University of California, Los Angeles (UCLA), Los Angeles, CA, United States

Post-traumatic epilepsy (PTE) prediction is one of the greatest challenges in recent years. The probability of developing PTE is strongly connected with injury severity. Accordingly, having an automated alternative to clinician scoring, to measure injury severity, could be helpful to measure the progression of the disease in view of finding PTE biomarkers. Therefore, we have conducted a study aimed to evaluate if injury severity can be established from automatic analyses of MRI data in a way comparable to manual clinical scoring. We found a statistical association between morphological features and two clinical scores used to quantify injury severity.

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