Advanced morphological analysis of cerebral blood flow for acute concussion diagnosis and return-to-play determination

项目摘要/摘要 仅在美国，每年就有160到380万人遭受轻微的脑外伤。可靠的诊断和及时 治疗对于处理由轻度脑外伤引起的通常严重的短期和长期后遗症至关重要。 然而，一种可靠、客观和准确的方法在医院外和医院内诊断轻度脑损伤 尤其是在确定RTP准备情况方面，一直没有得到临床界的认可。当前诊断和RTP 评估基于患者的症状、神经认知评估和/或体能表现 测试。症状量表的使用是有问题的，有几个原因，包括主观性和可靠性。 神经认知评估和身体测试(如平衡测试)，虽然主观性较低，但需要 由于评估对象之间固有的较大差异，受试者的伤害基线测试 表演。由于这些原因，目前的轻度脑外伤诊断方法应用有限，而且 不适用于绝大多数轻度脑外伤患者。 本项目旨在开发一种对轻型创伤性脑损伤(轻度脑损伤)的客观诊断，其基础是 患者受伤后的生理变化，并提供了一个能够进行RTP指导的平台。方法是 基于脑震荡后众所周知的生理变化的量化，即自主神经的损害 应用经颅多谱勒(TCD)检测脑功能及脑血流量(CBF)变化。的新奇之处 建议的方法是使用最近开发的分析性机器学习框架来 脑血流速度(CBFV)波形分析。与以前使用的方法不同，拟议的 该方法利用复杂的CBFV波形的整个形状，从而获得血流的细微变化 在其他分析方法中丢失的数据。此外，我们的平台与MRI之间的全面验证 将在受伤后进行，从而导致这种类型的第一次科学实验。 此第二阶段SBIR的最终目标是将客观和准确的软件算法商业化 目前尚不存在的运动性脑震荡的可靠诊断和处理。结果将是 集成到现有TCD中的软件套件，将销售给急诊科、神经科 诊所，以及参与轻度脑损伤诊断和RTP管理的其他医疗保健提供者。

Project Summary / Abstract Between 1.6 and 3.8 million people each year suffer a mild TBI in the US alone. Reliable diagnosis and prompt treatments are vital to managing the often-serious short and long-term sequelae resulting from mild TBI. However, a reliable objective and accurate method for mild TBI diagnosis outside of a hospital setting, and in particular for determining RTP readiness, has eluded the clinical community. Current diagnosis and RTP assessments are based on patient symptoms, neurocognitive evaluations, and / or physical performance testing. Use of symptom scales are problematic for several reasons including subjectivity and reliability. Neurocognitive evaluations and physical tests (such as balance tests), although less subjective, require pre- injury baseline testing of subjects due to inherently large subject-to-subject variations in evaluation performances. Due to these reasons, current mild TBI diagnostic methods have limited applications and are not suitable for a significant majority of patients who suffer mild TBI. This project is aimed at developing an objective diagnosis of mild traumatic brain injury (mild TBI) based on physiologic changes in a patient after injury and providing a platform capable of RTP guidance. The method is based on quantification of well-known physiologic changes after a concussion, i.e. the impairment of autonomic function and altered cerebral blood flow (CBF) as measured with transcranial Doppler (TCD). The novelty of the proposed approach is the use of a recently-developed analytical machine learning framework for the analysis of the CBF velocity (CBFV) waveforms. In contrast to previous methods used before, the proposed approach utilizes the entire shape of the complex CBFV waveform, thus obtaining subtle changes in blood flow that are lost in other analysis methods. Additionally, comprehensive verification between our platform and MRI will be performed following injury resulting in the first scientific experiments of this kind. The ultimate goal of this Phase II SBIR is to commercialize an objective and accurate software algorithm for reliable diagnosis and management of sports concussions which does not currently exist. The outcome will be a software suite integrated into existing TCD and will be marketed to emergency departments, neurology clinics, and other healthcare providers involved in mild TBI diagnosis and RTP management.