用脖子上的声音信号无创检测吞咽障碍，准确率超90%
Automated Dysphagia Screening Using Noninvasive Neck Acoustic Sensing
- 通过采集吞咽时颈部声波，结合机器学习自动识别异常
- 在5次独立划分下AUC达0.904，性能良好
- 适合老年人群或居家筛查，无需放射检查
咽喉健康对呼吸、吞咽和发声等基本功能至关重要。早期发现吞咽障碍（即吞咽困难）对于及时干预极为关键。然而，现有诊断方法多依赖影像学检查或侵入性手段。本研究提出一种基于便携式非侵入声学传感与机器学习相结合的自动化吞咽障碍检测框架。通过捕捉吞咽过程中颈部产生的微弱声学信号，识别与异常生理状态相关的模式。该方法在5次独立训练-测试划分下达到0.904的AUC-ROC值，展现了良好的异常检测性能。研究证明了非侵入声学传感在咽喉健康监测中的可行性与实用性，具备可扩展性和临床应用前景。
原文摘要 · Abstract (English)
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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