Steffensen Torjus L, Bartnes Barge, Fuglstad Maja L, Auflem Marius, Steinert Martin
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Mechanical Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
Front Robot AI. 2023 Oct 30;10:1218174. doi: 10.3389/frobt.2023.1218174. eCollection 2023.
In emergency medicine, airway management is a core skill that includes endotracheal intubation (ETI), a common technique that can result in ineffective ventilation and laryngotracheal injury if executed incorrectly. We present a method for automatically generating performance feedback during ETI simulator training, potentially augmenting training outcomes on robotic simulators. Electret microphones recorded ultrasonic echoes pulsed through the complex geometry of a simulated airway during ETI performed on a full-size patient simulator. As the endotracheal tube is inserted deeper and the cuff is inflated, the resulting changes in geometry are reflected in the recorded signal. We trained machine learning models to classify 240 intubations distributed equally between six conditions: three insertion depths and two cuff inflation states. The best performing models were cross validated in a leave-one-subject-out scheme. Best performance was achieved by transfer learning with a convolutional neural network pre-trained for sound classification, reaching global accuracy above 98% on 1-second-long audio test samples. A support vector machine trained on different features achieved a median accuracy of 85% on the full label set and 97% on a reduced label set of tube depth only. This proof-of-concept study demonstrates a method of measuring qualitative performance criteria during simulated ETI in a relatively simple way that does not damage ecological validity of the simulated anatomy. As traditional sonar is hampered by geometrical complexity compounded by the introduced equipment in ETI, the accuracy of machine learning methods in this confined design space enables application in other invasive procedures. By enabling better interaction between the human user and the robotic simulator, this approach could improve training experiences and outcomes in medical simulation for ETI as well as many other invasive clinical procedures.
在急诊医学中，气道管理是一项核心技能，其中包括气管插管（ETI），这是一种常见技术，如果操作不当可能导致通气无效和喉气管损伤。我们提出了一种在ETI模拟器训练期间自动生成性能反馈的方法，这可能会提高机器人模拟器的训练效果。驻极体麦克风记录了在全尺寸患者模拟器上进行ETI时通过模拟气道复杂几何结构产生的超声波回声脉冲。随着气管内导管插入得更深且套囊充气，几何结构的变化会反映在记录的信号中。我们训练机器学习模型对240次插管进行分类，这些插管在六种条件下平均分布：三种插入深度和两种套囊充气状态。性能最佳的模型采用留一受试者法进行交叉验证。通过使用针对声音分类预训练的卷积神经网络进行迁移学习，在1秒长的音频测试样本上实现了高于98%的全局准确率。在不同特征上训练的支持向量机在完整标签集上的中位数准确率为85%，在仅管深度的简化标签集上为97%。这项概念验证研究展示了一种以相对简单的方式在模拟ETI期间测量定性性能标准的方法，且不会损害模拟解剖结构的生态有效性。由于传统声纳受ETI中引入设备带来的几何复杂性的阻碍，机器学习方法在这种受限设计空间中的准确性使其能够应用于其他侵入性操作。通过实现人类用户与机器人模拟器之间更好的交互，这种方法可以改善ETI以及许多其他侵入性临床操作的医学模拟训练体验和效果。
