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.

翻译：咽部健康在呼吸、吞咽及发声等关键人体功能中起着至关重要的作用。早期发现吞咽异常（亦称吞咽困难）对于及时干预至关重要。然而，现有的诊断方法通常依赖于放射成像或有创操作。本研究提出一种自动化框架，通过便携式无创声学传感结合应用机器学习来检测吞咽困难。通过捕捉吞咽任务期间颈部产生的细微声学信号，我们旨在识别与异常生理状态相关的模式。我们的方法在测试时取得了良好的异常检测性能，在5次独立的训练-测试划分下AUC-ROC达到0.904。这项工作证明了使用无创声学传感作为咽部健康监测的实用且可扩展工具的可行性。