Automated Decision Support System for Traumatic Brain Injury through Image Processing and Machine Learning Approaches

总结： 目前迫切需要一个自动化的决策支持系统，用于创伤性疾病的诊断和预后。 脑损伤（TBI）。TBI是现代世界的主要死亡原因之一， to disability残疾and impairment损伤. TBI的早期检测及其适当的管理提出了一个未满足的需求。 因此，我们的目标是通过开发一个用于监测的决策支持系统来补充临床医生的决策 以及整合TBI患者的可用信息以进行准确和定量的诊断和预后。 这个项目是建立一个系统，创造个性化的长期目标的主要组成部分。 治疗计划。具体来说，我们打算自动检测和准确量化两个关键的 异常包括大脑中间结构的移位（目标1）和颅内出血（目标2）， 计算机断层扫描（CT）头部扫描。在目标1中，我们开发了一个用于描绘大脑空间转移的模型 结构及其预测能力。我们采用解剖标志来检测3D变形表面的 脑损伤后的大脑中线这样的方法使我们能够量化移动的体积，这是一种无法量化的测量方法。 目前可以实现。此外，它提供了准确和及时的访问传统中线移位在2D CT切片。在目标2中，我们建立了一个描述颅内出血及其预测能力的模型。我们 实现3D卷积神经网络模型以检测出血区域并量化和定位 他们的体积。目前，这些测量是不准确的，并且由于繁琐的测量而不易获得。 手动过程;相反，2D CT切片中的病变厚度用于评估其严重性。在目标1和 2、我们自动计算常规和建议的体积和位置测量， 比较它们以建议每种异常的最佳诊断指标。最后，在目标3中，我们构建了一个 TBI严重程度评估和结果预测的自动化管道。为此，手动CT扫描读取 将与电子健康记录中的患者信息相结合， 数据驱动的诊断和预后。我们实施机器学习方法来构建能够 预测短期和长期临床结果。我们的预测模型将独立于 图像处理算法在实现目标1和2后，自动计算信息， CT扫描将被纳入机器学习模型。这项研究意义重大，因为它 预计将推进TBI护理，特别是在受伤后的“黄金时间”内。最终，这样的系统 有可能减少延误和漏诊，从而降低TBI发病率和死亡率。 此外，通过防止永久性和/或继发性损伤，并最大限度地减少住院时间， 康复，我们的系统将有助于减少美国每年760亿美元的TBI护理负担。 除了创新的建议方法及其定量输出，我们汇总了四个现有的 将异质性纳入表型和治疗的数据集，因此所得模型将 可推广并适用于真实的临床环境。

Summary: There is an urgent need for an automated decision support system for diagnosis and prognosis of traumatic brain injuries (TBI). TBI is one of the leading causes of death in the modern world, and substantially contributes to disability and impairment. The early detection of TBI and its proper management presents an unfilled need. We therefore aim to supplement clinicians' decisions by developing a decision support system for monitoring and integrating available information of a TBI patient for accurate and quantitative diagnosis and prognosis. This project is the main component of a long-term goal of building a system that creates personalized treatment plans. Specifically, we intend to automatically detect and accurately quantify two critical abnormalities including shift in the brain's middle structure (Aim 1) and intracranial hemorrhage (Aim 2) from computed tomography (CT) head scans. In Aim 1, we develop a model for delineating the spatial shift in brain structure and its predictive power. We employ anatomical landmarks to detect a 3D deformed surface of the brain midline after TBI. Such an approach allows us to quantify the shifted volume, a measurement that is not currently achievable. Additionally, it provides accurate and timely access to conventional midline shift in a 2D CT slice. In Aim 2, we build a model for delineating intracranial hemorrhage and its predictive power. We implement a 3D convolutional neural network model to detect hemorrhagic regions and quantify and localize their volume. Currently, these measurements are inaccurate and not readily available due to the cumbersome manual process; instead a lesion's thickness in a 2D CT slice is used to assess its severity. In both Aim 1 and 2, we automatically calculate conventional and proposed volumetric and locational measurements and compare them to suggest the best diagnostic metric for each abnormality. Finally, in Aim 3, we build an automated pipeline for TBI severity assessment and outcome prediction. To this end, manual CT scan reads will be integrated with patient-level information available from electronic health records to achieve accurate data-driven diagnosis and prognosis. We implement machine learning approaches to build models capable of predicting short and long-term clinical outcomes. Our prediction models will be developed independently of our image processing algorithms. Upon achievement of Aims 1 and 2, automatically calculated information from CT scans will be incorporated into machine learning models. The proposed research is significant, because it is expected to advance TBI care, specifically within the “golden hour" post-injury. Ultimately, such a system has the potential to reduce delayed and missed diagnosis, thereby reducing TBI morbidity and mortality. Additionally, by preventing permanent and/or secondary injuries, and minimizing the time of hospitalization and rehabilitation, our system will contribute to reducing the annual $76 billion burden of TBI care in the U.S. In addition to innovation in the proposed approaches and their quantitative outputs, we aggregate four existing datasets to incorporate heterogeneity in both phenotypes and therapies, so the resulted model will be generalizable and applicable to real clinical settings.