Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Med Eng Phys. 2011 Oct;33(8):1017-26. doi: 10.1016/j.medengphy.2011.04.006. Epub 2011 May 8.
Endotracheal intubation is a complex medical procedure in which a ventilating tube is inserted into the human trachea. Improper positioning carries potentially fatal consequences and therefore confirmation of correct positioning is mandatory. This paper introduces a novel system for endotracheal tube position confirmation. The proposed system comprises a miniature complementary metal oxide silicon sensor (CMOS) attached to the tip of a semi rigid stylet and connected to a digital signal processor (DSP) with an integrated video acquisition component. Video signals are acquired and processed by a confirmation algorithm implemented on the processor. The confirmation approach is based on video image classification, i.e., identifying desired expected anatomical structures (upper trachea and main bifurcation of the trachea) and undesired structures (esophagus). The desired and undesired images are indicators of correct or incorrect endotracheal tube positioning. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture models (GMMs), estimated using a greedy algorithm. A multi-dimensional feature space, which consists of several textural-based features, is utilized to represent the images. The performance of the proposed algorithm was evaluated using two datasets: a dataset of 1600 images extracted from 10 videos recorded during intubations on dead cows, and a dataset of 358 images extracted from 8 videos recorded during intubations performed on human subjects. Each one of the video images was classified by a medical expert into one of three categories: upper tracheal intubation, correct (carina) intubation and esophageal intubation. The results, obtained using a leave-one-case-out method, show that the system correctly classified 1530 out of 1600 (95.6%) of the cow intubations images, and 351 out of the 358 human images (98.0%). Misclassification of an image of the esophagus as carina or upper-trachea, which is potentially fatal, was extremely rare (only one case when in the animal dataset and no cases when in the human intubation dataset). The classification results of the cow intubations dataset compare favorably with a state-of-the-art classification method tested on the same dataset.
经口气管插管是一种复杂的医疗程序，其中将通气管插入人体气管。不当的定位可能会带来致命的后果，因此必须确认正确的定位。本文介绍了一种新的经气管插管位置确认系统。该系统包括一个微型互补金属氧化物硅传感器（CMOS），附着在半刚性引导丝的尖端，连接到一个带有集成视频采集组件的数字信号处理器（DSP）。视频信号由处理器上实现的确认算法采集和处理。确认方法基于视频图像分类，即识别所需的预期解剖结构（上气管和气管的主要分支）和不期望的结构（食管）。期望和不期望的图像是正确或不正确的经气管插管定位的指示。所提出的方法包括使用高斯混合模型（GMM）的连续和概率图像表示方案，该方案使用贪婪算法进行估计。多维特征空间由几个基于纹理的特征组成，用于表示图像。使用从在死牛上进行的插管记录的 10 个视频中提取的 1600 个图像数据集和从在人体上进行的插管记录的 8 个视频中提取的 358 个图像数据集评估了所提出算法的性能。每个视频图像都由医学专家分类为三个类别之一：上气管插管、正确（隆突）插管和食管插管。使用离开一个病例的方法获得的结果表明，该系统正确分类了 1600 个牛插管图像中的 1530 个（95.6%），以及 358 个人类图像中的 351 个（98.0%）。将食管图像错误分类为隆突或上气管，这是潜在致命的，非常罕见（在动物数据集中有一个案例，在人类插管数据集没有案例）。牛插管数据集的分类结果与在同一数据集上测试的最先进的分类方法相比表现良好。
