Meeting Banner
Abstract #3276

Improved Outcome prediction in mild Traumatic Brain Injury using Latent Feature Extraction from Volumetric MRI

Sanjay Purushotham1, Ashwathy Samivel Sureshkumar1, Li Jiang2, Shiyu Tang2, Steven Roys2, Chandler Sours Rhodes2,3, Rao P. Gullapalli2, and Jiachen Zhuo2
1Department of Information System, University of Maryland, Baltimore County, Baltimore, MD, United States, 2Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, 3National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, United States

Mild traumatic brain injury (mTBI) patients account for more than 70% of all TBI, with a subset experiencing persistent post concussive symptoms more than 3 months post injury. Patients clinical presentation and conventional imaging findings acutely post injury often lack the ability to predict chronic outcome. In this study, we present a novel method for latent feature extraction from volumetric MRI. We show that with machine learning methods, these acute latent volumetric MRI features are able to improve our symptom prediction in mTBI patients at 18 months post injury.

This abstract and the presentation materials are available to members only; a login is required.

Join Here