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

项目概要/摘要 仅在美国，每年就有 1.6 至 380 万人遭受轻度 TBI。可靠的诊断和提示 治疗对于控制轻度 TBI 引起的严重短期和长期后遗症至关重要。 然而，在医院环境之外以及在医院内诊断轻度 TBI 的可靠、客观和准确的方法 特别是用于确定 RTP 准备情况的方法，一直困扰着临床界。目前的诊断和RTP 评估基于患者症状、神经认知评估和/或身体表现 测试。由于主观性和可靠性等多种原因，症状量表的使用存在问题。 神经认知评估和身体测试（例如平衡测试）虽然不太主观，但需要预先进行 由于评估中受试者之间固有的巨大差异，对受试者进行损伤基线测试 表演。由于这些原因，目前的轻度TBI诊断方法应用有限，难以推广。 不适合大多数患有轻度 TBI 的患者。 该项目旨在基于以下数据开发轻度创伤性脑损伤（轻度 TBI）的客观诊断： 患者受伤后的生理变化，并提供能够进行 RTP 指导的平台。方法是 基于对脑震荡后众所周知的生理变化（即自主神经损伤）的量化 使用经颅多普勒 (TCD) 测量的功能和脑血流量 (CBF) 改变。的新颖性 所提出的方法是使用最近开发的分析机器学习框架 CBF 速度 (CBFV) 波形分析。与之前使用的方法相比，提出的 该方法利用复杂 CBFV 波形的整个形状，从而获得血流的细微变化 这是其他分析方法中丢失的。此外，我们的平台和 MRI 之间进行了全面验证 将在受伤后进行，这是此类科学实验的首次。 第二阶段 SBIR 的最终目标是将客观且准确的软件算法商业化 目前尚不存在对运动脑震荡的可靠诊断和管理。结果将是 集成到现有 TCD 中的软件套件，将销往急诊科、神经科 诊所和其他参与轻度 TBI 诊断和 RTP 管理的医疗保健提供者。

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.