Deep Learning and Fluid Dynamics Based Phenotyping of Expiratory Central Airway Collapse

基于深度学习和流体动力学的呼气中央气道塌陷表型分析

• 批准号: 10013198
• 负责人: Surya P. Bhatt
• 金额: $ 18.7万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-09 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10013198
• 关键词: 3-Dimensional; 3D Print; Adult; Affect; Age; Air Movements; Airway Resistance; Area; Body mass index; Breathing; Caliber; Chronic; Chronic Obstructive Airway Disease; Clinical; Complex; Data; Data Set; Databases; Disease; Distal; Dyspnea; Exhalation; Forced expiratory volume function; Frequencies; Generations; Geometry; Goals; Image; Individual; Intervention; Length; Liquid substance; Lung diseases; Maps; Measurement; Methods; Modeling; Neural Network Simulation; Outcome; Patient-Focused Outcomes; Patients; Phenotype; Physiological; Probability; Pulmonary Emphysema; Pulmonary Function Test/Forced Expiratory Volume 1; Quality of life; Race; Resistance; Scanning; Site; Smoker; Smoking; Spirometry; Stents; Symptoms; Testing; Therapeutic Intervention; Trachea; Training; Translations; Tube; United States; Visualization; X-Ray Computed Tomography; airway obstruction; base; cartilaginous; cigarette smoking; clinical application; clinical predictors; clinically significant; convolutional neural network; deep learning; expiration; human subject; individual patient; insight; patient subsets; personalized medicine; pressure; respiratory; respiratory morbidity; response; sex; simulation; three-dimensional modeling

Project Summary/ Abstract Expiratory central airway collapse (ECAC), defined by >50% collapse of large airways during expiration, resulting from either cartilaginous weakening or redundancy of the posterior membranous wall of the trachea, is an increasingly recognized disorder associated with cigarette smoking and chronic obstructive pulmonary disease (COPD). Airflow obstruction in smokers primarily arises from increased resistance to airflow in the small distal conducting airways <2 mm in diameter. It is plausible that in a subset of smokers with and without COPD, central airway collapse results in additional resistance to airflow, resulting in substantial respiratory morbidity. Ninety-two million adults in the Unites States are active or past smokers, and ECAC is present in approximately 5% of current and former smokers. The presence of ECAC is associated with greater dyspnea, worse respiratory-quality of life and greater frequency of exacerbations after adjustment for underlying lung disease. Whether these patients will benefit from interventional therapies such as stenting or tracheopexy depends on whether the airflow resistance caused by ECAC contributes to symptoms, and this in turn depends on the relative contribution of central and small airways to overall airflow resistance. If the overall airflow resistance is primarily due to distal small airways obstruction in a given patient with ECAC, treating central airway collapse is unlikely to benefit such a patient. Our central hypothesis is that ECAC results in additional airflow obstruction beyond that incurred in the small airways, and that in a subset of patients the central airways are the major site of airflow obstruction and hence are amenable to therapy. The complex interplay of proximal and distal airway resistances and transpulmonary pressures does not lend itself to direct measurements in human subjects across a range of physiological pressure and flow changes. We propose a combination of CT-derived imaging and patient-personalized benchtop model and deep learning to answer these questions with the following specific aims. Aim 1 of this application will be to derive personalized patient- specific information on airway geometry and resistance using airway segmentation from computed tomography (CT) scans. We will calculate airway resistances in central and small airways using standard formulae. The goal of Aim 2 is to create bench-top simulations to understand the complex interplay between the resistance of small and large airways. In Aim 3, we will use deep learning to derive probability scores for clinically substantial ECAC from segmented airway images on computed tomography. The results of our study will enable patient-specific personalized therapies for ECAC. The mechanistic insights gained from this study will help identify patients with clinically significant ECAC and hence most likely to benefit from therapeutic interventions.

项目概要/摘要 呼气中央气道塌陷 (ECAC)，定义为呼气期间大气道塌陷 >50%， 由于气管后膜壁软骨弱化或冗余造成， 是一种日益被认识的与吸烟和慢性阻塞性肺疾病有关的疾病 疾病（慢性阻塞性肺病）。吸烟者的气流阻塞主要是由于吸烟者体内气流阻力增加所致 直径 <2 毫米的小远端传导气道。在有或没有吸烟的吸烟者中，这似乎是合理的。 慢性阻塞性肺病（COPD）、中央气道塌陷会导致额外的气流阻力，导致严重的呼吸困难 发病率。 美国有 9200 万成年人是活跃或过去吸烟者，ECAC 存在于 大约 5% 的当前和曾经吸烟者。 ECAC 的存在与更大的呼吸困难有关， 调整基础肺部后，呼吸生活质量更差，病情恶化的频率更高 疾病。这些患者是否会受益于支架置入术或气管固定术等介入治疗 取决于 ECAC 引起的气流阻力是否会导致症状，而这又取决于 中央气道和小气道对整体气流阻力的相对贡献。如果整体气流 阻力主要是由于 ECAC 患者的远端小气道阻塞，治疗中枢性 气道塌陷不太可能使此类患者受益。我们的中心假设是 ECAC 会导致额外的 气流阻塞超出了小气道中发生的气流阻塞，以及部分患者中枢气道中发生的气流阻塞 气道是气流阻塞的主要部位，因此适合治疗。复杂的相互作用 近端和远端气道阻力和跨肺压不适合直接 对人体进行一系列生理压力和流量变化的测量。我们提出一个 结合 CT 衍生成像和患者个性化台式模型以及深度学习来回答 这些问题有以下具体目的。该应用程序的目标 1 是获得个性化的患者- 使用计算机断层扫描气道分割获得有关气道几何形状和阻力的具体信息 （CT）扫描。我们将使用标准公式计算中央气道和小气道的气道阻力。这 目标 2 的目标是创建台式模拟，以了解电阻之间的复杂相互作用 小型和大型航空公司。在目标 3 中，我们将使用深度学习来得出临床实质性的概率分数 ECAC 来自计算机断层扫描分段气道图像。 我们的研究结果将为 ECAC 提供针对患者的个性化治疗。机制见解 从这项研究中获得的结果将有助于识别具有临床意义的 ECAC 的患者，因此最有可能受益 来自治疗干预。