Non-Invasive Sensor-Based Estimation of Anterior-Posterior Upper Esophageal Sphincter Opening Maximal Distension.
GRU
Swallowing
accelerometry
aspiration
attention mechanisms
cervical auscultation
deep learning
dysphagia
recurrent neural networks
signal analysis
supervised learning
upper esophageal sphincter
vibrations
Journal
IEEE journal of translational engineering in health and medicine
ISSN: 2168-2372
Titre abrégé: IEEE J Transl Eng Health Med
Pays: United States
ID NLM: 101623153
Informations de publication
Date de publication:
2023
2023
Historique:
received:
25
07
2022
revised:
25
01
2023
accepted:
15
02
2023
entrez:
6
3
2023
pubmed:
7
3
2023
medline:
8
3
2023
Statut:
epublish
Résumé
Dysphagia management relies on the evaluation of the temporospatial kinematic events of swallowing performed in videofluoroscopy (VF) by trained clinicians. The upper esophageal sphincter (UES) opening distension represents one of the important kinematic events that contribute to healthy swallowing. Insufficient distension of UES opening can lead to an accumulation of pharyngeal residue and subsequent aspiration which in turn can lead to adverse outcomes such as pneumonia. VF is usually used for the temporal and spatial evaluation of the UES opening; however, VF is not available in all clinical settings and may be inappropriate or undesirable for some patients. High resolution cervical auscultation (HRCA) is a noninvasive technology that uses neck-attached sensors and machine learning to characterize swallowing physiology by analyzing the swallow-induced vibrations/sounds in the anterior neck region. We investigated the ability of HRCA to noninvasively estimate the maximal distension of anterior-posterior (A-P) UES opening as accurately as the measurements performed by human judges from VF images. Trained judges performed the kinematic measurement of UES opening duration and A-P UES opening maximal distension on 434 swallows collected from 133 patients. We used a hybrid convolutional recurrent neural network supported by attention mechanisms which takes HRCA raw signals as input and estimates the value of the A-P UES opening maximal distension as output. The proposed network estimated the A-P UES opening maximal distension with an absolute percentage error of 30% or less for more than 64.14% of the swallows in the dataset. This study provides substantial evidence for the feasibility of using HRCA to estimate one of the key spatial kinematic measurements used for dysphagia characterization and management. Clinical and Translational Impact Statement: The findings in this study have a direct impact on dysphagia diagnosis and management through providing a non-invasive and cheap way to estimate one of the most important swallowing kinematics, the UES opening distension, that contributes to safe swallowing. This study, along with other studies that utilize HRCA for swallowing kinematic analysis, paves the way for developing a widely available and easy-to-use tool for dysphagia diagnosis and management.
Identifiants
pubmed: 36873304
doi: 10.1109/JTEHM.2023.3246919
pmc: PMC9976940
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
182-190Subventions
Organisme : NICHD NIH HHS
ID : R01 HD092239
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD074819
Pays : United States
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