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.

翻译：创伤性脑损伤（TBI）是一项重大的全球公共卫生挑战，导致高发病率和死亡率，并给全球医疗系统带来沉重经济负担。TBI的诊断依赖于临床信息及计算机断层扫描（CT）影像。为应对TBI带来的多重难题，人们开发出针对这一复杂病症的创新性数据驱动方法。尤其值得注意的是，轻度创伤性脑损伤（mTBI）在TBI病例中占比最高，而传统方法在此类病例中常显不足。为此，我们综述了当前应用于TBI临床信息和CT影像的最新机器学习（ML）技术，特别关注mTBI领域。我们根据数据来源对ML应用进行分类，并梳理了迄今使用的多种ML技术。这些技术大多主要聚焦于诊断，而针对预后预测的探索相对较少。本综述旨在为未来利用数据驱动方法及标准诊断数据改进TBI诊断的研究提供思路借鉴。