Neonatal Endotracheal Intubation: Enhancing Training Through Computer Simulation and Automated Evaluation

项目摘要 新生儿气管插管(ETI)是一种时间敏感的复苏程序！对通风至关重要 新生儿。由于呼吸道狭窄，舌头相对较大，前部 声门位置和新生儿低呼吸储备(Bercic，Pocajt等人)。1978年)。考虑到这一困难， 由于手术过程和未受过训练的手的高并发症发生率，有效的训练至关重要。然而， 在目前的复苏培训计划下，儿科住院医生的插管成功率很低 在住院1-3年之间几乎没有改善(23%-25%)(O‘Donnell，Kamlin等人)。2006年；巴里等人的哈布纳 艾尔2013)。迫切需要了解导致培训效果不佳的因素和 创新的培训模式，可以弥补传统培训留下的差距，从而实现快速技能 收购。 我们假设当前的培训和评估方法存在四个主要缺陷：(1)质量差 真实感：基于人体模型和模拟器的训练通常在解剖学或难度级别上提供很少的变化-关键 开发专门知识的要求(德莱弗斯、阿塔纳修等人)1986)-并不现实地模拟 真实组织的外观、感觉和运动。(二)考核主观性、变数大、资源密集型 方法：培训机会受到专家指导员的限制。(3)可视化效果差： 学习者对哪里出了问题以及如何改进知之甚少；他们看不清到底是什么 在模特或患者体内进行，不能直接监控他们相对于理想化的、专家的行为 性能。(4)人为理想条件下的评价：ETI课堂成绩评价 环境可能高估了受训者的技能水平，因为他们没有模仿出压力和分散注意力的因素 是真实的临床环境中固有的。 技术增强的ETI模拟器可以解决所有这些关键弱点：我们已经进行了 前期工作(Hahn，Li等人2016年；Soghier，Li等人。2014)关于增强现实(AR(Azuma 1997)) 由实习生的运动和实时的物理人体模型驱动的Manikin模拟器，1)提供 ETI技术的定量评估和2)允许受训者可视化喉镜的运动 在人体模型里。评估分数可以在表演期间提供反馈，以及构成 评估实习生技能的一部分。根据这项提案开展的工作将建立在这项前期工作的基础上。这个 具体目标是：将目前的增强现实(AR)人体模拟器扩展到虚拟现实(VR) 计算机模拟器，并验证、扩展和验证自动评估和可视化算法 ETI，通过测试为期3年的儿科住院医生小组来研究培训有效性，以量化 在插管性能方面，相对于目前的训练方案，技术改进的方法 关于模拟器和患者的临床结果，并评估在更现实的条件下的表现。

Project Summary Neonatal endotracheal intubation (ETI) is a time-sensitive resuscitation procedure! essential for ventilation of newborns. It requires an unusually high level of skill due to the narrow airways, relatively large tongue, anterior glottic position, and low respiratory reserve of neonates (Bercic, Pocajt et al. 1978). Given the difficulty of the procedure and the high rate of complications in untrained hands, effective training is crucial. However, intubation success rates for pediatric residents are low under current resuscitation training programs and show little improvement between years 1-3 of residency (23-25%) (O'Donnell, Kamlin et al. 2006; Haubner, Barry et al. 2013). There is a pressing need to understand the factors that lead to poor training results and for innovative training modalities that can bridge the gap left by traditional training and thereby allow rapid skill acquisition. We hypothesize that current training and assessment methods suffer from 4 key weaknesses: (1) Poor realism: manikin and simulator-based training typically provide little variation in anatomy or difficulty level—key requirements for developing expertise (Dreyfus, Athanasiou et al. 1986)—and do not realistically model the look, feel, and motions of real tissue. (2) Subjective, highly variable, and resource-intensive assessment methods: training opportunities are limited by the availability of expert instructors. (3) Poor visualization: learners have poor knowledge about what went wrong and how to improve; they cannot see exactly what is going on inside the manikin or the patient and cannot directly monitor their actions relative to idealized, expert performance. (4) Assessment under artificially ideal conditions: assessments of ETI performance in classroom settings likely overestimate trainees' skill level because they do not mimic the stressors and distractions that are inherent in the real clinical environment. Technology-enhanced ETI simulators can resolve all of these key weaknesses: We have conducted preliminary work (Hahn, Li et al. 2016; Soghier, Li et al. 2014) on an augmented reality (AR (Azuma 1997)) manikin simulator driven by the motions of the trainee and physical manikin in real time that 1) provides a quantitative assessment of ETI technique and 2) allows the trainee to visualize the motion of the laryngoscope inside the manikin. The assessment score can provide feedback during the performance, as well as constitute part of the evaluation of the trainee's skill. Work under this proposal will build on this preliminary work. The specific aims are to: extend the current augmented reality (AR) manikin simulator to a virtual reality (VR) computer simulator and validate, extend and validate automated assessment and visualization algorithm for ETI, study training effectiveness by testing groups of pediatric residents across 3 years to quantify the effect of technology-enhanced methods relative to the current training regimen in terms of both intubation performance on simulators and clinical outcomes in patients, and assess performance under more realistic conditions.