A NOVEL ARTIFICIAL INTELLIGENCE SYSTEM FOR ENDOTRACHEAL INTUBATION
A NOVEL ARTIFICIAL INTELLIGENCE SYSTEM FOR ENDOTRACHEAL INTUBATION
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DOI：
10.3109/10903127.2016.1139220
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发表时间：
2016-09-01
影响因子：
2.4
通讯作者：
Yealy, Donald M.
中科院分区：
文献类型：
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作者：
Carlson, Jestin N.;Das, Samarjit;Yealy, Donald M.
目的：充分显示声门开口是成功进行气管内插管(ETI)的关键因素；然而，很少有客观工具能够实时引导提供者尝试使用声门开口进行ETI。机器学习/人工智能有助于自动检测其他视觉结构，但其对ETI的效用尚不清楚。我们试图测试各种计算机算法在识别声门开口方面的准确性，创造出一种可以帮助成功插管的工具。方法：我们收集了一个方便的样本，每个提供者使用视频喉镜(C-MAC，Karl Storz Corp，Tuttlingen，德国)在人体模型上进行10次ETI。我们记录了每一次尝试，并回顾了一秒钟的时间间隔，以确定是否有声门开口。四种不同的机器学习/人工智能算法分析了每一次尝试和时间点：K最近邻(KNN)、支持向量机(SVM)、决策树和神经网络(NN)。我们用一半的视频来训练算法，另一半来测试每种算法的准确性、敏感性和特异性。结果：我们招募了7名提供者、3名急诊主治医师和4名护理专业学生。从总共记录的70次喉镜视频尝试中，我们创建了2465个时间间隔。对于声门开口的检测，算法的灵敏度和特异度如下：KNN(70%，90%)、支持向量机(70%，90%)、决策树(68%，80%)和神经网络(72%，78%)。结论：使用人工智能的计算机算法的初步努力能够以80%以上的准确率识别声门开口。随着进一步的改进，视频喉镜有可能向提供者提供实时的方向反馈，以帮助指导成功的ETI。
Objective: Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers' ETI attempts toward the glottic opening in real-time. Machine learning/artificial intelligence has helped to automate the detection of other visual structures but its utility with ETI is unknown. We sought to test the accuracy of various computer algorithms in identifying the glottic opening, creating a tool that could aid successful intubation.Methods: We collected a convenience sample of providers who each performed ETI 10times on a mannequin using a video laryngoscope (C-MAC, Karl Storz Corp, Tuttlingen, Germany). We recorded each attempt and reviewed one-second time intervals for the presence or absence of the glottic opening. Four different machine learning/artificial intelligence algorithms analyzed each attempt and time point: k-nearest neighbor (KNN), support vector machine (SVM), decision trees, and neural networks (NN). We used half of the videos to train the algorithms and the second half to test the accuracy, sensitivity, and specificity of each algorithm. Results: We enrolled seven providers, three Emergency Medicine attendings, and four paramedic students. From the 70 total recorded laryngoscopic video attempts, we created 2,465 time intervals. The algorithms had the following sensitivity and specificity for detecting the glottic opening: KNN (70%, 90%), SVM (70%, 90%), decision trees (68%, 80%), and NN (72%, 78%). Conclusions: Initial efforts at computer algorithms using artificial intelligence are able to identify the glottic opening with over 80% accuracy. With further refinements, video laryngoscopy has the potential to provide real-time, direction feedback to the provider to help guide successful ETI.