Upper Esophageal Sphincter Opening Segmentation with Convolutional Recurrent Neural Networks in High Resolution Cervical Auscultation
Yassin Khalifa, Cara Donohue, James L. Coyle, Ervin Sejdi\'c

TL;DR
This study develops a deep neural network method to accurately segment upper esophageal sphincter opening in swallowing using non-invasive cervical auscultation, matching human ratings and potentially replacing radiographic assessments.
Contribution
The paper introduces a novel convolutional recurrent neural network approach for non-invasive, high-resolution detection of esophageal sphincter opening, demonstrating high accuracy and clinical relevance.
Findings
Achieved over 90% accuracy in sphincter opening segmentation
Predicted opening and closure moments within human inter-rater error
Validated method on independent clinical data
Abstract
Upper esophageal sphincter is an important anatomical landmark of the swallowing process commonly observed through the kinematic analysis of radiographic examinations that are vulnerable to subjectivity and clinical feasibility issues. Acting as the doorway of esophagus, upper esophageal sphincter allows the transition of ingested materials from pharyngeal into esophageal stages of swallowing and a reduced duration of opening can lead to penetration/aspiration and/or pharyngeal residue. Therefore, in this study we consider a non-invasive high resolution cervical auscultation-based screening tool to approximate the human ratings of upper esophageal sphincter opening and closure. Swallows were collected from 116 patients and a deep neural network was trained to produce a mask that demarcates the duration of upper esophageal sphincter opening. The proposed method achieved more than 90\%…
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