Automated Assessment of Neonatal Endotracheal Intubation Measured by a Virtual Reality Simulation System.

Automated Assessment of Neonatal Endotracheal Intubation Measured by a Virtual Reality Simulation System.
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DOI:
10.1109/embc44109.2020.9176629
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发表时间:
2020-07
期刊:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子:
--
通讯作者:
Hahn J
Hahn J
中科院分区:
其他
文献类型:
--
作者:
Xiao X;Zhao S;Zhang X;Soghier L;Hahn J

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在新生儿气管插管(ETI)培训中,专家的手动评估是一个耗时且繁琐的过程。这种主观的、高度可变的和资源密集型的评估方法不仅可能引入评估者间/评估者内的可变性,而且在许多大规模的培训项目中也表现出严重的局限性。此外,手术过程中的可视化较差,使指导者无法观察人体模型或患者体内发生的事件,这将额外的误差源引入评估中。在本文中,我们提出了一个基于物理学的虚拟现实(VR)ETI模拟系统,捕捉喉镜和气管插管(ETT)的整个运动与虚拟患者的内部解剖结构。我们的系统提供了一个完整的可视化过程,为教师提供全面的信息,以进行准确的评估。更重要的是,开发了一种可解释的机器学习算法,通过训练从运动中提取的性能参数和专家评分来自动评估ETI性能。我们的结果表明,留一交叉验证(LOOCV)的自动评估算法的分类准确率为80%,这表明我们的系统可以可靠地进行一致的和标准化的评估ETI培训。
Manual assessment from experts in neonatal endotracheal intubation (ETI) training is a time-consuming and tedious process. Such subjective, highly variable, and resource-intensive assessment method may not only introduce inter-rater/intra-rater variability, but also represent a serious limitation in many large-scale training programs. Moreover, poor visualization during the procedure prevents instructors from observing the events occurring within the manikin or the patient, which introduces an additional source of error into the assessment. In this paper, we propose a physics-based virtual reality (VR) ETI simulation system that captures the entire motions of the laryngoscope and the endotracheal tube (ETT) in relation to the internal anatomy of the virtual patient. Our system provides a complete visualization of the procedure, offering instructors with comprehensive information for accurate assessment. More importantly, an interpretable machine learning algorithm was developed to automatically assess the ETI performance by training on the performance parameters extracted from the motions and the scores rated by experts. Our results show that the leave-one-out-cross-validation (LOOCV) classification accuracy of the automated assessment algorithm is 80%, which indicates that our system can reliably conduct a consistent and standardized assessment for ETI training.