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Abstract #0895

Biomarkers for CTE diagnosis in retired NFL player using Machine learning

Marcia Louis1, Michael Alosco2, Benjamin Rowland3, Huijin Liao3, Joseph Wang4, Ajay Joshi4, Robert Stern2, and Alexander Lin3

1Electrical and Computer Engineering, Boston University, Boston, MA, United States, 2Department of Neurology, Boston University, Boston, MA, United States, 3Center for Clinical Spectroscopy, Brigham and Women's hospital, Boston, MA, United States, 4Boston University, Boston, MA, United States

Multiple concussions have the potential to develop Chronic Traumatic Encephalopathy (CTE), a neurodegenerative disease that is currently diagnosed only in postmortem by tau protein deposition in the brain. Since repetitive head trauma alters brain morphology and metabolism, magnetic resonance imaging and spectroscopy could be suitable candidates for CTE diagnosis. Therefore, we propose machine learning-based approach to identify CTE-related biomarkers. The model achieves 80% prediction accuracy with AUC of 0.72 using creatine, macromolecules and brain volume as features for the machine learning model.

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