# Automated Dysphagia Screening Using Noninvasive Neck Acoustic Sensing

arXiv: https://arxiv.org/abs/2602.02725 · HTML: https://mlsanity.com/paper/2602.02725/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking](https://mlsanity.com/paper/2406.16148/) (2024)
- [Easy, Interpretable, Effective: openSMILE for voice deepfake detection](https://mlsanity.com/paper/2408.15775/) (2024)

## Abstract (arXiv)

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.