  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.
