Zhao Shang, Xiao Xiao, Zhang Xiaoke, Yan Meng Wei Li, Soghier Lamia, Hahn James K
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5455-5458. doi: 10.1109/EMBC44109.2020.9176329.
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 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-Out-Cross-Validation (LOOCV).
新生儿气管插管（ETI）是一项重要且复杂的复苏技能，需要大量练习才能掌握。目前的ETI练习是在实体模型上进行的，并且依赖于专家教员的评估。由于培训机会受到专家教员可用性的限制，因此非常需要一种自动评估模型。然而，由于识别关键特征、提供准确评估以及向学员提供有价值反馈的复杂性，实现ETI评估自动化具有挑战性。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受训人员提供总体评分和性能反馈。所提出的评估模型从我们开发的基于模型的增强型ETI系统中获取捕获的运动学多变量时间序列（MTS）数据，自动提取捕获数据的关键特征，并最终提供总体评分作为输出。此外，基于类激活映射（CAM）的可视化可以自动识别对总体评分有重大影响的动作，从而为学员提供有用的反馈。使用留一法交叉验证（LOOCV），我们的模型平均分类准确率可以达到92.2%。
