Presentation + Paper
12 April 2021 On the robustness of high-resolution cervical auscultation-based detection of upper esophageal sphincter opening duration in diverse populations
Author Affiliations +
Abstract
Swallowing dysfunction, or dysphagia, occurs secondary to many underlying etiologies such as stroke and can lead to pneumonia. The upper esophageal sphincter (UES) is a major anatomical landmark that allows the passage of swallowed materials into the esophagus during swallowing. Delayed UES opening or reduced duration of opening can lead to the accumulation of pharyngeal residue, which can increase risk of aspiration. UES opening is observed through the inspection of radiographic exams, known as videofluoroscopy swallow studies (VFSSs), which expose patients to ionizing radiation and depend on subjective clinician interpretations. High resolution cervical auscultation (HRCA) is a non-invasive sensor-based technology that has been recently investigated to depict swallowing physiology. HRCA has been proposed for detecting UES opening duration through a deep learning framework. However, the proposed framework was only validated over swallows from patients. For such an algorithm to be robust, it has to be proven equally reliable for the detection of UES opening duration in swallows from both patients and healthy subjects. In this study, we intend to investigate the robustness of the HRCA-based framework to detect the UES opening in signals collected from a diverse population. The framework showed comparable performance regarding the UES opening detection with an average area under the ROC curve of 95%. The results indicate that the HRCA-based UES opening detection can provide superior performance on swallows from diverse populations which demonstrates the clinical potential of HRCA as a non-invasive swallowing assessment tool.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yassin Khalifa, Cara Donohue, James L. Coyle, and Ervin Sejdić "On the robustness of high-resolution cervical auscultation-based detection of upper esophageal sphincter opening duration in diverse populations", Proc. SPIE 11730, Big Data III: Learning, Analytics, and Applications, 117300M (12 April 2021); https://doi.org/10.1117/12.2588591
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Cited by 1 scholarly publication.
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KEYWORDS
Imaging systems

Neck

Network architectures

Neural networks

Signal detection

Ionizing radiation

Machine learning

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