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名新生儿通过气管内导管接受机械通气 (ETT)每年都有100万人死亡。新生儿插管是具有挑战性的，因为他们 不幸的是，近40%的初始插管尝试 是不正确的，管是无意中放置在食道，而不是 气管，或在主干支气管太深，导致只有一个肺通气， 或者管的尖端在气管中太高。关键是要检测错位 管及时。本研究的目标是发展同时多源 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。在本申请中，我们将联合收割机（1） 基于深度学习EIT的ETT放置确认（2）肺部EIT图像 通风。总之，这将为临床医生和床边工作人员提供一个真实的- 时间，闭环系统，用于确定（1）ETT是否插入正确的 管腔（气管，不是食道）和（2）如果肺被适当地通气， 检测左或右主支气管错位。相同的系统，没有变化 可以用于监测无意拔管和 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， 由在身体上的电极上测量的电压形成， 施加电流。由于EIT是一种安全、便携、无损伤的技术， 效果，它可以用于连续监测和根据需要。我们 来自通用电气研究院、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。第一个目的是开发和实现一种电极配置， 重建算法和GE SMS-EIT系统的硬件修改， 新生儿的特殊需求和这个项目。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的，食管，太 高位或主干支气管错位。的疗效和临床 的SMS-EIT系统和算法的实时检测和 将在一项纳入30名婴儿的研究中评价ETT错位的分类， 斯坦福大学医学中心的IV NICU。