Real-time noninvasive visualization of endotracheal tube placement and 3D lung monitoring in infants with electrical impedance tomography

通过电阻抗断层扫描实时无创可视化婴儿气管插管放置和 3D 肺部监测

• 批准号: 10456497
• 负责人: Jennifer Lynn Mueller
• 金额: $ 83.58万
• 依托单位: COLORADO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10456497
• 关键词: 3-Dimensional; Academic Medical Centers; Algorithms; Bedside Technology; Blinded; Bronchi; Cessation of life; Classification; Clinical; Colorado; Data; Databases; Detection; Electrodes; Emergency Situation; Enrollment; Esophagus; Functional Imaging; Goals; Hypoxemia; Image; Imaging Techniques; Infant; Intubation; Ionizing radiation; Lead; Left; Lung; Maps; Measures; Mechanical ventilation; Modification; Monitor; Nature; Neonatal Intensive Care Units; Network-based; Newborn Infant; Observational Study; Operating Rooms; Output; Patients; Pneumothorax; Positioning Attribute; Probability; Radiation; Research; Roentgen Rays; Saline; Scanning; Source; System; Techniques; Technology; Time; Trachea; Training; Tube; United States; Universities; Visualization; X-Ray Computed Tomography; adverse outcome; base; carina; classification algorithm; clinical decision-making; convolutional neural network; deep learning; deep learning algorithm; electrical impedance tomography; endotracheal; lung imaging; models and simulation; neonate; novel; portability; prevent; prospective; public health relevance; real-time images; reconstruction; side effect; skills; standard of care; stem; success; ultrasound; ventilation; voltage

PROJECT SUMMARY Over 100,000 newborns receive mechanical ventilation through an endotracheal tube (ETT) each year in the United States. Intubating newborns is challenging due to their size and delicate nature, and unfortunately, nearly 40% of the initial intubation attempts are incorrect, and the tube is inadvertently placed in the esophagus instead of the trachea, or too deep in the main stem bronchus, leading to ventilation of only one lung, or with the tip of the tube too high in the trachea. It is critical to detect malpositioning of the tube promptly. The goal of this research is to develop Simultaneous Multi-Source Electrical Impedance Tomography (SMS-EIT) technology for the bedside to correctly and instantly identify ETT position or malposition. In this application we will combine (1) deep learning EIT-based confirmation of ETT placement with (2) EIT images of lungs being ventilated. Together, this would provide clinicians and bedside staff with a real- time, closed-loop system for determining if (1) the ETT was inserted in the correct lumen (trachea, not esophagus) and (2) if the lungs are being ventilated appropriately to detect left or right mainstem bronchial malplacement. The same system with no change in electrode placement could be used to monitor for inadvertent extubation and for the onset of emergency conditions such as pneumothorax. EIT is a noninvasive, non-ionizing functional imaging technique in which images are formed from voltages measured on electrodes on the body arising from imperceptible applied currents. Since EIT is a safe and portable technology with no damaging side effects, it can be used both for continuous monitoring and as needed. Our interdisciplinary team from GE Research, Colorado State University, and Stanford University will develop and validate the specialized SMS-EIT system through three specific aims. The first aim is to develop and implement an electrode configuration, reconstruction algorithms, and hardware modifications of the GE SMS-EIT system for the special needs of neonates and this project. In the second aim, training data and a deep learning classification algorithm to classify intubation as correct, esophageal, too high, or mainstem bronchial misplacement will be developed. The efficacy and clinical feasibility of the SMS-EIT system and algorithms for the real-time detection and classification of ETT malplacement will be evaluated in a study of 30 infants in the Level IV NICU at Stanford University Medical Center.

项目概要 超过 100,000 名新生儿通过气管插管接受机械通气 (ETT) 每年在美国举行。新生儿插管具有挑战性，因为他们 尺寸和脆弱性，不幸的是，近 40% 的初始插管尝试 是不正确的，并且管子无意中放置在食道中而不是食道中 气管，或主干支气管太深，导致只有一个肺通气， 或者管子尖端在气管中过高。检测错位至关重要 立即插管。本研究的目标是开发同步多源 电阻抗断层扫描 (SMS-EIT) 技术可帮助床边正确诊断 并立即识别 ETT 位置或错位。在此应用程序中，我们将结合 (1) 使用 (2) 个肺部 EIT 图像进行基于深度学习 EIT 的 ETT 放置确认 正在通风。总之，这将为临床医生和床边工作人员提供真正的 时间，闭环系统，用于确定 (1) ETT 是否插入正确的位置 管腔（气管，不是食道）和 (2) 肺部是否适当通气以 检测左或右主支气管错位。相同的系统，没有任何改变 电极放置可用于监测意外拔管和 出现气胸等紧急情况。 EIT 是一种非侵入性、非电离功能成像技术，其中图像 由在身体电极上测量的电压形成，该电压是由不可察觉的 施加的电流。由于 EIT 是一种安全、便携的技术，没有任何破坏性的一面 效果，它既可以用于连续监测，也可以根据需要使用。我们的 来自 GE Research、科罗拉多州立大学和斯坦福大学的跨学科团队 大学将通过三个方面开发和验证专门的 SMS-EIT 系统 具体目标。第一个目标是开发和实施电极配置， GE SMS-EIT 系统的重建算法和硬件修改 新生儿的特殊需要和这个项目。在第二个目标中，训练数据和 深度学习分类算法将插管分类为正确、食管插管 高，或会发生主支气管错位。疗效及临床 SMS-EIT系统和实时检测算法的可行性 ETT 错位的分类将在一项对 30 名婴儿进行的研究中进行评估 斯坦福大学医学中心的 IV NICU。