ArticleScientific reports2020
Machine learning analysis to automatically measure response time of pharyngeal swallowing reflex in videofluoroscopic swallowing study.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 51 citations in OpenAlex.
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- Multi-Center Validation of Artificial Intelligence-Based Video Analysis Platform for Automatic Evaluation of Swallowing Disorders.Diagnostics (Basel, Switzerland) · 2025Article
- A Machine Learning Pipeline for Automated Bolus Segmentation and Area Measurement in Swallowing Videofluoroscopy Images of an Infant Pig Model.Dysphagia · 2025Article
- An intelligent quantitative analysis software for videofluoroscopic swallowing study in patients with dysphagia.Quantitative imaging in medicine and surgery · 2025Article
- Relationship Between Oral Thickness Perception and Food Dynamics During Liquid Swallowing in Healthy Young Adults.Journal of oral rehabilitation · 2025Article
- Artificial Intelligence in Videofluoroscopy Swallow Study Analysis: A Comprehensive Review.Dysphagia · 2025Review
- Post-stroke dysphagia: identifying the evidence missing.Frontiers in medicine · 2025Review
- Clinical Use of Nomogram Based on Machine Learning for Diagnosis Prediction of Acute Respiratory Distress Syndrome in Patients With Acute Pancreatitis.Mediators of inflammation · 2025Article
- Article
- The Development of an Artificial Intelligence Video Analysis-Based Web Application to Diagnose Oropharyngeal Dysphagia: A Pilot Study.Brain sciences · 2024Article
- Oropharyngeal swallowing hydrodynamics of thin and mildly thick liquids in an anatomically accurate throat-epiglottis model.Scientific reports · 2024Article
- Application of deep learning technology for temporal analysis of videofluoroscopic swallowing studies.Scientific reports · 2023Article
- Detection of aspiration from images of a videofluoroscopic swallowing study adopting deep learning.Oral radiology · 2023Article
- AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome.Sensors (Basel, Switzerland) · 2022Article
- Synchronization between videofluoroscopic swallowing study and surface electromyography in patients with neurological involvement presenting symptoms of dysphagiaBiomedica : revista del Instituto Nacional de Salud · 2022Article
- A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing.Scientific reports · 2022Article
- Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?Computer methods and programs in biomedicine · 2022Article
- Usefulness of the Modified Videofluoroscopic Dysphagia Scale in Determining the Allowance of Oral Feeding in Patients with Dysphagia Due to Deconditioning or Frailty.Healthcare (Basel, Switzerland) · 2022Article
- Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study.Journal of Korean medical science · 2022Article
- Effects of Presbyphagia on Oropharyngeal Swallowing Observed during Modified Barium Swallow Studies.The journal of nutrition, health & aging · 2022Article
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate clinical features and determine rehabilitation strategies of dysphagia, it is crucial to measure the exact response time of the pharyngeal swallowing reflex in a videofluoroscopic swallowing study (VFSS). However, measuring the response time of the pharyngeal swallowing reflex is labor-intensive and particularly for inexperienced clinicians, it can be difficult to measure the brief instance of the pharyngeal swallowing reflex by VFSS. To accurately measure the response time of the swallowing reflex, we present a novel framework, able to detect quick events. In this study, we evaluated the usefulness of machine learning analysis of a VFSS video for automatic measurement of the response time of a swallowing reflex in a pharyngeal phase. In total, 207 pharyngeal swallowing event clips, extracted from raw VFSS videos, were annotated at the starting point and end point of the pharyngeal swallowing reflex by expert clinicians as ground-truth. To evaluate the performance and generalization ability of our model, fivefold cross-validation was performed. The average success rates of detection of the class "during the swallowing reflex" for the training and validation datasets were 98.2% and 97.5%, respectively. The average difference between the predicted detection and the ground-truth at the starting point and end point of the swallowing reflex was 0.210 and 0.056 s, respectively. Therefore, the response times during pharyngeal swallowing reflex are automatically detected by our novel framework. This framework can be a clinically useful tool for estimating the absence or delayed response time of the swallowing reflex in patients with dysphagia and improving poor inter-rater reliability of evaluation of response time of pharyngeal swallowing reflex between expert and unskilled clinicians.
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