ArticleIEEE journal of biomedical and health informatics2021
Upper Esophageal Sphincter Opening Segmentation With Convolutional Recurrent Neural Networks in High Resolution Cervical Auscultation.
Article in IEEE journal of biomedical and health informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04468191 (Quantifying Fatigue of the Respiratory and Swallowing Musculature in Patients With Amyotrophic Lateral Sclerosis), which is not on this map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Quantifying Fatigue of the Respiratory and Swallowing Musculature in Patients With Amyotrophic Lateral Sclerosis
Who cites it
23 citing papers in PubMed, 47 citations in OpenAlex.
- Artificial intelligence tools for the assessment and management of dysphagia: a scoping review.BMJ open · 2026Article
- Multimodal LLM-driven IoT digital healthcare platform for intelligent dysphagia dietary monitoring.Frontiers in nutrition · 2026Article
- An intelligent quantitative analysis software for videofluoroscopic swallowing study in patients with dysphagia.Quantitative imaging in medicine and surgery · 2025Article
- Novel Videographic-Free Framework for Tracking Anatomical Structures Using Swallowing Accelerometer Signals and Multi-task Transformers.Journal of healthcare informatics research · 2025Article
- Non-invasive integrated swallowing kinematic analysis framework leveraging transformer-based multi-task neural networks.Computers in biology and medicine · 2025Article
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- sEMG-based automatic characterization of swallowed materials.Biomedical engineering online · 2024Article
- Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients.IEEE journal of translational engineering in health and medicine · 2024Article
- A Review of Recurrent Neural Network-Based Methods in Computational Physiology.IEEE transactions on neural networks and learning systems · 2023Review
- Temporal Sequence of Laryngeal Vestibule Closure and Reopening is Associated With Airway Protection.The Laryngoscope · 2023Article
- Autonomous Swallow Segment Extraction Using Deep Learning in Neck-Sensor Vibratory Signals From Patients With Dysphagia.IEEE journal of biomedical and health informatics · 2023Article
- Non-Invasive Sensor-Based Estimation of Anterior-Posterior Upper Esophageal Sphincter Opening Maximal Distension.IEEE journal of translational engineering in health and medicine · 2023Article
- Cervical Vertebral Height Approximates Hyoid Displacement in Videofluoroscopic Images of Healthy Adults.Dysphagia · 2022Article
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- Article
- Improving Non-Invasive Aspiration Detection With Auxiliary Classifier Wasserstein Generative Adversarial Networks.IEEE journal of biomedical and health informatics · 2022Article
- A Preliminary Investigation of Similarities of High Resolution Cervical Auscultation Signals Between Thin Liquid Barium and Water Swallows.IEEE journal of translational engineering in health and medicine · 2022Article
- DMFF-Net: A dual encoding multiscale feature fusion network for ovarian tumor segmentation.Frontiers in public health · 2022Article
- Characterizing Swallows From People With Neurodegenerative Diseases Using High-Resolution Cervical Auscultation Signals and Temporal and Spatial Swallow Kinematic Measurements.Journal of speech, language, and hearing research : JSLHR · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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% accuracy and similar values of sensitivity and specificity when compared to human ratings even when tested over swallows from an independent clinical experiment. Moreover, the predicted opening and closure moments surprisingly fell within an inter-human comparable error of their human rated counterparts which demonstrates the clinical significance of high resolution cervical auscultation in replacing ionizing radiation-based evaluation of swallowing kinematics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.