Automated Dysphagia Screening Using Noninvasive Neck Acoustic Sensing

2026-02-02

AI Summary

This study addresses the limitations of current dysphagia screening methods, which rely heavily on radiographic imaging or invasive procedures, by proposing a non-invasive, automated approach based on neck acoustic sensing and machine learning. By capturing acoustic signals during swallowing and integrating advanced signal processing with classification models, the method enables portable, radiation-free, and low-cost assessment of swallowing function for the first time. Evaluated across five independent train-test splits, the system achieves an AUC-ROC of 0.904 in detecting swallowing abnormalities, demonstrating both its technical efficacy and strong potential for clinical translation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Speech
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Machine learning and data science for the Web

Abstract

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.

Problem

• dysphagia
• swallowing disorder
• noninvasive screening
• pharyngeal health
• early detection

Innovation

• noninvasive acoustic sensing
• dysphagia screening
• machine learning
• swallowing disorder detection
• pharyngeal health monitoring