Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making ‘go/no-go’ decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute and early-stage mTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

轻度创伤性脑损伤 (mTBI) 是最常见的脑损伤类型。及时诊断 mTBI 对于做出“去/不去”决定至关重要，以防止重复受伤，避免可能延长恢复时间的剧烈活动，并确保受试者的高水平表现能力。如果未确诊，mTBI 可能会导致各种短期和长期异常，包括但不限于认知功能受损、疲劳、抑郁、易怒和头痛。现有的筛查和诊断工具来检测急性和早期mTBI 的敏感性和特异性不足。这导致关于诊断和恢复活动或需要进一步医疗的临床决策的不确定性。因此，重要的是确定相关的生理生物标志物，这些生物标志物可以整合到一个相互补充的集合中，并提供数据模式的组合，以提高 mTBI 的现场诊断灵敏度。近年来，可穿戴医疗设备的处理能力、信号保真度以及记录通道和模式的数量得到了极大的提高，并产生了大量的数据。在同一时期，机器学习工具和数据处理方法取得了令人难以置信的进步。这些成就使临床医生和工程师能够为 mTBI 开发和实施多参数高精度诊断工具。在这篇综述中，我们首先评估急性 mTBI 诊断中的临床挑战，然后考虑用于评估可能与 mTBI 相关的生理生物标志物的各种传感技术的记录方式和硬件实现。最后，我们讨论了基于机器学习的 mTBI 检测的最新技术，并考虑了更多样化的定量生理生物标志物特征列表如何改进当前的数据驱动方法，为 mTBI 患者提供及时的诊断和治疗。然后考虑用于评估可能与 mTBI 相关的生理生物标志物的各种传感技术的记录方式和硬件实现。最后，我们讨论了基于机器学习的 mTBI 检测的最新技术，并考虑了更多样化的定量生理生物标志物特征列表如何改进当前的数据驱动方法，为 mTBI 患者提供及时的诊断和治疗。然后考虑用于评估可能与 mTBI 相关的生理生物标志物的各种传感技术的记录方式和硬件实现。最后，我们讨论了基于机器学习的 mTBI 检测的最新技术，并考虑了更多样化的定量生理生物标志物特征列表如何改进当前的数据驱动方法，为 mTBI 患者提供及时的诊断和治疗。