ArticleFuture generations computer systems : FGCS2021
Estimation of laryngeal closure duration during swallowing without invasive X-rays.
Article in Future generations computer systems : FGCS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06637774 (The Aspirometer), which is not on this map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The Aspirometer: A Noninvasive Tool for Detecting Aspiration
Who cites it
21 citing papers in PubMed, 32 citations in OpenAlex.
- Enhancing the assessment of deglutition function in preterm infants with mechano-acoustic analysis and machine learning: a narrative review.Perspectives of the ASHA special interest groups · 2026Article
- Using Machine Learning for the Automated Segmentation and Detection of Swallows Obtained by Digital Cervical Auscultation in Preterm Neonates.Dysphagia · 2026Article
- Acoustic and Perceptual Profiles of Swallowing Sounds in Preterm Neonates: A Cross-Sectional Study Cohort.Dysphagia · 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
- Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Comment on Chatwin et al. Waves of Precision: A Practical Guide for Reviewing New Tools to Evaluate Mechanical In-Exsufflation Efficacy in Neuromuscular Disorders.Journal of clinical medicine · 2024Article
- 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
- Pilot Study: Magnetic Motion Analysis for Swallowing Detection Using MEMS Cantilever Actuators.Sensors (Basel, Switzerland) · 2023Article
- 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
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- Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?Computer methods and programs in biomedicine · 2022Article
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- Improving Non-Invasive Aspiration Detection With Auxiliary Classifier Wasserstein Generative Adversarial Networks.IEEE journal of biomedical and health informatics · 2022Article
- Effects of Presbyphagia on Oropharyngeal Swallowing Observed during Modified Barium Swallow Studies.The journal of nutrition, health & aging · 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
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Laryngeal vestibule (LV) closure is a critical physiologic event during swallowing, since it is the first line of defense against food bolus entering the airway. Identifying the laryngeal vestibule status, including closure, reopening and closure duration, provides indispensable references for assessing the risk of dysphagia and neuromuscular function. However, commonly used radiographic examinations, known as videofluoroscopy swallowing studies, are highly constrained by their radiation exposure and cost. Here, we introduce a non-invasive sensor-based system, that acquires high-resolution cervical auscultation signals from neck and accommodates advanced deep learning techniques for the detection of LV behaviors. The deep learning algorithm, which combined convolutional and recurrent neural networks, was developed with a dataset of 588 swallows from 120 patients with suspected dysphagia and further clinically tested on 45 samples from 16 healthy participants. For classifying the LV closure and opening statuses, our method achieved 78.94% and 74.89% accuracies for these two datasets, suggesting the feasibility of implementing sensor signals for LV prediction without traditional videofluoroscopy screening methods. The sensor supported system offers a broadly applicable computational approach for clinical diagnosis and biofeedback purposes in patients with swallowing disorders without the use of radiographic examination.
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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.