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.

项目摘要 超过100,000名新生儿通过气管管接收机械通风 （ETT）每年在美国。由于他们的 大小和微妙的性质，不幸的是，近40％的初始插管尝试 是不正确的，并且管被无意间放在食道而不是 气管或在主茎支气管中太深，导致仅通气一个肺， 或带管的尖端在气管中过高。检测到不正确的 管子迅速。这项研究的目的是共同开发同时的多源 床边的电阻抗断层扫描（SMS-EIT）技术正确 并立即确定ETT位置或错误。在此应用中，我们将结合（1） 基于（2）肺的EIT图像对ETT放置的深度学习确认 被通风。这将为临床医生和床头员工提供现实 时间，闭环系统，用于确定（1）是否将ETT插入正确 管腔（气管，不是食道）和（2）如果肺被适当通风至 检测左或右主系统支气管畸形。没有更改的同一系统 在电极中，可用于监视无意的拔管和 紧急状况（例如气胸）的发作。 EIT是一种无创的，非电离的功能成像技术，其中图像是 由在无法察觉的体内的电极上测量的电压形成 施加电流。由于EIT是一项安全且便携的技术，没有破坏性的一面 效果，它既可以根据需要进行连续监视。我们的 GE研究，科罗拉多州立大学和斯坦福大学的跨学科团队 大学将通过三个开发和验证专业的SMS-EIT系统 具体目标。第一个目的是开发和实施电极配置， 重建算法和用于GE SMS-EIT系统的硬件修改 新生儿和该项目的特殊需求。在第二个目标中，培训数据和 深度学习分类算法也将插管分类为正确的食管 将开发高或主要的支气管错位。功效和临床 SMS-EIT系统和实时检测算法的可行性和 将在对30位婴儿的研究中评估ETT损坏的分类 IV NICU在斯坦福大学医学中心。