A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling

TL;DR

This paper develops a machine learning framework that uses multi-shell diffusion MRI features from specific brain regions to accurately classify patients with mild traumatic brain injury, addressing a key diagnostic challenge.

Contribution

It introduces a novel machine learning approach utilizing multi-shell diffusion MRI data from targeted brain regions for MTBI classification.

Findings

Effective classification of MTBI patients achieved

Identified key brain regions associated with injury

Demonstrated potential for improved diagnosis

Abstract

While diffusion MRI has been extremely promising in the study of MTBI, identifying patients with recent MTBI remains a challenge. The literature is mixed with regard to localizing injury in these patients, however, gray matter such as the thalamus and white matter including the corpus callosum and frontal deep white matter have been repeatedly implicated as areas at high risk for injury. The purpose of this study is to develop a machine learning framework to classify MTBI patients and controls using features derived from multi-shell diffusion MRI in the thalamus, frontal white matter and corpus callosum.