Laryngeal vestibule (LV) closure is a critical physiologic event during swallowing, since it is the first line of defense against food bolus entering the airway. Identifying the laryngeal vestibule status, including closure, reopening and closure duration, provides indispensable references for assessing the risk of dysphagia and neuromuscular function. However, commonly used radiographic examinations, known as videofluoroscopy swallowing studies, are highly constrained by their radiation exposure and cost. Here, we introduce a non-invasive sensor-based system, that acquires high-resolution cervical auscultation signals from the neck and accommodates advanced deep learning techniques for the detection of LV behaviors. The deep learning algorithm, which combined convolutional and recurrent neural networks, was developed with a dataset of 588 swallows from 120 patients with suspected dysphagia and further clinically tested on 45 samples from 16 healthy participants. For classifying the LV closure and opening statuses, our method achieved 78.94% and 74.89% accuracies for these two datasets, suggesting the feasibility of implementing sensor signals for LV prediction without traditional videofluoroscopy screening methods. The sensor supported system offers a broadly applicable computational approach for clinical diagnosis and biofeedback purposes in patients with swallowing disorders without the use of radiographic examination.

中文翻译：

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无需侵入性 X 射线即可估计吞咽过程中喉部闭合持续时间

喉前庭 (LV) 闭合是吞咽过程中的一个关键生理​​事件，因为它是防止食物团进入气道的第一道防线。识别喉前庭状态，包括闭合、重新开放和闭合持续时间，为评估吞咽困难和神经肌肉功能的风险提供了不可或缺的参考。然而，常用的放射线检查，即视频透视吞咽检查，受到辐射暴露和成本的高度限制。在这里，我们介绍了一种基于传感器的非侵入性系统，该系统从颈部获取高分辨率颈部听诊信号，并采用先进的深度学习技术来检测左心室行为。该深度学习算法结合了卷积神经网络和循环神经网络，利用来自 120 名疑似吞咽困难患者的 588 次吞咽的数据集开发而成，并对来自 16 名健康参与者的 45 个样本进行了进一步的临床测试。为了对左心室关闭和打开状态进行分类，我们的方法对这两个数据集实现了 78.94% 和 74.89% 的准确度，这表明在不使用传统视频透视筛查方法的情况下实施传感器信号进行左心室预测的可行性。该传感器支持的系统提供了一种广泛适用的计算方法，用于吞咽障碍患者的临床诊断和生物反馈目的，而无需使用放射线检查。