Smartphone-based intelligent system with a wearable throat microphone for real-time screening of swallowing ability.

Abstract

Objectives: Accurate and timely swallowing ability screening is essential for effective prevention and treatment of dysphagia. Current clinical imaging methods are often radioactive or invasive, limiting their applicability for routine monitoring. This study aims to develop a smartphone-based intelligent system for real-time screening of swallowing ability using a wearable throat microphone.

Methods: First, a customized Android application was developed to collect, visualize, and analyze sounds related to swallowing from a wearable throat microphone. Second, a transfer learning model, YAMNet-S, was trained on 4,715 one-second audio segments of coughing, swallowing, and other noises, obtained from 215 healthy participants (Experiment 1). The trained model was then deployed on a smartphone to classify swallowing-related events in real time. Finally, the water swallow test (WST) was conducted by counting swallowing and coughing events within 30 s from 15 simulated patients with self-induced voluntary coughs for clinical validation (Experiment 2).

Results: The mean accuracy of the trained model in swallowing and coughing events classification is 94.48 and 94.45 %, respectively, and the difference of WST scores in system calculations and expert evaluation was 0.267 (out of 5).

Conclusions: This smartphone-based intelligent system has the potential to be a comfort, portability and user-friendliness tool for preliminary screening of dysphagia before VFSS/FEES, especially on some situations with limited medical resources, such as community or home-based care.