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

项目总结 100,000多名新生儿通过气管插管接受机械通气 (ETT)在美国每年。给新生儿插管具有挑战性，因为他们 大小和脆弱的性质，不幸的是，近40%的初始插管尝试 都是不正确的，并且管子无意中被放置在食道中，而不是 气管，或主干支气管处太深，导致只有一个肺通风， 或者管子的尖端在气管里太高。检测错误的位置是至关重要的 及时拔掉管子。本研究的目标是开发同时多源 电阻抗断层成像(SMS-EIT)技术用于床边正确 并立即识别ETT位置或错位。在本应用程序中，我们将结合(1) 基于深度学习EIT的(2)肺部EIT图像对ETT定位的确认 处于通风状态。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 管腔(气管，而不是食道)和(2)如果肺被适当地通风到 发现左或右主干支气管处错位。相同的系统，没有任何变化 电极放置可以用来监测意外拔管和 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 由在人体电极上测量的电压形成的，由不可察觉的 外加电流。由于EIT是一种安全、便携的技术，没有任何破坏性 效果，它既可用于持续监测，也可根据需要使用。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 大学将通过三个阶段开发和验证专门的SMS-EIT系统 明确的目标。第一个目标是开发和实现一种电极配置， GE SMS-EIT系统的重建算法和硬件修改 新生儿的特殊需求和这一项目。在第二个目标中，训练数据和一个 深度学习分类算法将插管分类为正确的，也是食道的 高位或主干支气管错位将会发生。白花蛇舌草的疗效及临床应用 短消息EIT系统的可行性及实时检测算法的研究 ETT错位的分类将在一项对30名婴儿的研究中进行评估 斯坦福大学医学中心的IV NICU。

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.