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

通过图像处理和机器学习方法治疗创伤性脑损伤的自动决策支持系统

基本信息

批准号：
9757500
负责人：
Negar Farzaneh
金额：
$ 3.72万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-10 至 2021-07-09
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9757500
关键词：
3-Dimensional Achievement Admission activity Algorithms American Anatomy Area Brain Caregivers Caring Cause of Death Characteristics Clinical Clinical Data Consumption Data Data Science Data Set Decision Making Decision Support Systems Development Diagnosis Diagnostic Early Diagnosis Electronic Health Record Epidemic Glasgow Outcome Scale Goals Head Hemorrhage Heterogeneity Hospitalization Hour Image Impairment Injury Intracranial Hemorrhages Investigation Lead Length Length of Stay Lesion Link Location Machine Learning Manuals Measurement Measures Medical center Methods Modeling Modernization Monitor Morbidity - disease rate Neural Network Simulation Operative Surgical Procedures Outcome Output Patients Performance Phase Phenotype Population Heterogeneity Positioning Attribute Process Quality of life Radiology Specialty Reading Rehabilitation therapy Research Resources Savings Scanning Severities Slice Software Tools Source Specific qualifier value Structure Surface Survivors System Technology Testing Thick Time Tomography, Computed, Scanners Trauma Traumatic Brain Injury Triage Visual X-Ray Computed Tomography accurate diagnosis base care costs convolutional neural network cost design diagnostic accuracy disability functional outcomes human error image processing improved injured innovation learning strategy machine learning algorithm mortality neural network algorithm outcome forecast outcome prediction personalized medicine predictive modeling prevent three-dimensional modeling treatment planning

项目摘要

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.

概括：迫切需要建立创伤性诊断和预后的自动决策支持系统脑损伤（TBI）。 TBI是现代世界中死亡的主要原因之一，并大大贡献残疾和障碍。对TBI的早期发现及其适当的管理提出了未满足的需求。因此，我们的目标是通过开发监视决策支持系统来补充临床医生的决定并整合TBI患者的可用信息，以进行准确和定量的诊断和预后。该项目是建立一种创建个性化系统的长期目标的主要组成部分治疗计划。特别是，我们打算自动检测并准确量化两个关键异常，包括大脑中间结构的转移（AIM 1）和颅内出血（AIM 2）计算机断层扫描（CT）头部扫描。在AIM 1中，我们开发了一个用于描述大脑空间变化的模型结构及其预测能力。我们采用解剖标记来检测3D变形的表面 TBI之后的大脑中线。这种方法使我们能够量化移位量，这是一个不是目前可以接受。此外，它提供了准确，及时的访问2D中常规中线移动 CT切片。在AIM 2中，我们建立了一个模型，用于描述颅内出血及其预测能力。我们实施3D卷积神经网络模型以检测出血区域并量化和本地化他们的音量。目前，这些测量值不准确，由于繁琐手动过程；取而代之的是，使用2D CT切片中病变的厚度来评估其严重性。在AIM 1和 2，我们自动计算常规和提议的体积和位置测量以及将它们比较，以建议每个绝对值的最佳诊断指标。最后，在AIM 3中，我们建立了一个 TBI严重性评估和结果预测的自动管道。为此，手册CT扫描读取将与来自电子健康记录可用的患者级信息集成以实现准确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我们的图像处理算法。在实现目标1和2后，从 CT扫描将纳入机器学习模型。拟议的研究很重要，因为它预计将在伤害后的“黄金小时”中推进TBI护理。最终，这样的系统有可能减少延迟和错过的诊断，从而降低TBI的发病率和死亡率。此外，通过防止永久性和/或继发伤害，并最大程度地减少住院时间和康复，我们的系统将有助于减少美国年度耗资760亿美元的TBI护理烧伤在拟议的方法及其定量产出中增加了创新，我们将四个现有在表型和疗法中纳入异质性的数据集，因此所得模型将是可推广，适用于实际临床环境。