Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI

TL;DR

This paper reviews machine learning techniques applied to clinical data and CT scans for diagnosing and predicting outcomes in traumatic brain injury, especially mild TBI, highlighting current methods and future research directions.

Contribution

It categorizes ML applications in TBI, emphasizing the focus on diagnosis over prognosis and providing a comprehensive overview of current state-of-the-art approaches.

Findings

ML techniques mainly focus on diagnosis of TBI.

Few ML models predict TBI prognosis.

Review highlights gaps and future directions in ML for TBI.

Abstract

Traumatic Brain Injury (TBI) poses a significant global public health challenge, contributing to high morbidity and mortality rates and placing a substantial economic burden on healthcare systems worldwide. The diagnosis of TBI relies on clinical information along with Computed Tomography (CT) scans. Addressing the multifaceted challenges posed by TBI has seen the development of innovative, data-driven approaches, for this complex condition. Particularly noteworthy is the prevalence of mild TBI (mTBI), which constitutes the majority of TBI cases where conventional methods often fall short. As such, we review the state-of-the-art Machine Learning (ML) techniques applied to clinical information and CT scans in TBI, with a particular focus on mTBI. We categorize ML applications based on their data sources, and there is a spectrum of ML techniques used to date. Most of these techniques have primarily focused on diagnosis, with relatively few attempts at predicting the prognosis. This review may serve as a source of inspiration for future research studies aimed at improving the diagnosis of TBI using data-driven approaches and standard diagnostic data.