Automated Assessment System for Neonatal Endotracheal Intubation Using Dilated Convolutional Neural Network.
Automated Assessment System for Neonatal Endotracheal Intubation Using Dilated Convolutional Neural Network.
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DOI：
10.1109/embc44109.2020.9176329
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Hahn JK
中科院分区：
文献类型：
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作者：
Zhao S;Xiao X;Zhang X;Yan Meng WL;Soghier L;Hahn JK
新生儿气管插管（ETI）是一项重要而复杂的复苏技能，需要大量的实践才能掌握。目前的ETI实践是在物理假人上进行的，并依赖于专家讲师的评估。由于培训的机会是有限的专家讲师的可用性，自动评估模型是非常可取的。然而，自动化ETI评估是具有挑战性的，因为识别关键功能，提供准确的评估和提供有价值的反馈给学员的复杂性。在本文中，我们提出了一种基于扩张卷积神经网络（CNN）的ETI评估模型，该模型可以自动为儿科学员提供整体评分和表现反馈。建议的评估模型采取捕获的运动学多变量时间序列（MTS）数据从基于人体模型的增强现实（AR）ETI系统，我们开发的，自动提取捕获的数据的关键特征，并最终提供一个整体得分作为输出。此外，基于类激活映射（CAM）的可视化可以自动识别对整体得分有显著影响的动作，从而为受训者提供有用的反馈。我们的模型可以达到92.2%的平均分类精度使用留一主题交叉验证（LOOCV）。
Neonatal endotracheal intubation (ETI) is an important, complex resuscitation skill, which requires a significant amount of practice to master. Current ETI practice is conducted on the physical manikin and relies on the expert instructors’ assessment. Since the training opportunities are limited by the availability of expert instructors, an automatic assessment model is highly desirable. However, automating ETI assessment is challenging due to the complexity of identifying crucial features, providing accurate evaluations and offering valuable feedback to trainees. In this paper, we propose a dilated Convolutional Neural Network (CNN) based ETI assessment model, which can automatically provide an overall score and performance feedback to pediatric trainees. The proposed assessment model takes the captured kinematic multivariate time-series (MTS) data from the manikin-based augmented reality (AR) ETI system that we developed, automatically extracts the crucial features of captured data, and eventually provides an overall score as output. Furthermore, the visualization based on the class activation mapping (CAM) can automatically identify the motions that have significant impact on the overall score, thus providing useful feedback to trainees. Our model can achieve 92.2% average classification accuracy using the Leave-One-Subject-Out-Cross-Validation (LOOCV).