非侵入式颈部声学传感用于自动吞咽障碍筛查
Automated Dysphagia Screening Using Noninvasive Neck Acoustic Sensing
AI总结：
本文提出利用非侵入式颈部声学传感与机器学习，实现吞咽障碍的自动化筛查，实验结果显示AUC-ROC达到0.904。
摘要：
咽部健康在呼吸、吞咽和发声等基本人类功能中起着至关重要的作用。早期检测吞咽异常，也称为吞咽困难，对于及时干预至关重要。然而，当前的诊断方法往往依赖于放射影像学成像或侵入性程序。在本研究中，我们提出了一种利用便携式且非侵入性的声学传感结合应用机器学习来检测吞咽困难的自动化框架。通过在吞咽任务中捕捉颈部的细微声学信号，我们旨在识别与异常生理状况相关的模式。我们的方法在5个独立的训练-测试划分下实现了有希望的测试时间异常检测性能，AUC-ROC达到0.904。本工作展示了使用非侵入性声学传感作为实际且可扩展的工具用于咽部健康监测的可行性。
英文摘要：
Pharyngeal health plays a vital role in essential human functions such as breathing, swallowing, and vocalization. Early detection of swallowing abnormalities, also known as dysphagia, is crucial for timely intervention. However, current diagnostic methods often rely on radiographic imaging or invasive procedures. In this study, we propose an automated framework for detecting dysphagia using portable and noninvasive acoustic sensing coupled with applied machine learning. By capturing subtle acoustic signals from the neck during swallowing tasks, we aim to identify patterns associated with abnormal physiological conditions. Our approach achieves promising test-time abnormality detection performance, with an AUC-ROC of 0.904 under 5 independent train-test splits. This work demonstrates the feasibility of using noninvasive acoustic sensing as a practical and scalable tool for pharyngeal health monitoring.
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