ArticleSensors (Basel, Switzerland)2019
Automatic Detection of the Pharyngeal Phase in Raw Videos for the Videofluoroscopic Swallowing Study Using Efficient Data Collection and 3D Convolutional Networks
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Multimodal framework for swallow detection in video-fluoroscopic swallow studies using manometric pressure distributions from dysphagic patients.International journal of computer assisted radiology and surgery · 2026Article
- Multi-Center Validation of Artificial Intelligence-Based Video Analysis Platform for Automatic Evaluation of Swallowing Disorders.Diagnostics (Basel, Switzerland) · 2025Article
- An intelligent quantitative analysis software for videofluoroscopic swallowing study in patients with dysphagia.Quantitative imaging in medicine and surgery · 2025Article
- Artificial Intelligence in Videofluoroscopy Swallow Study Analysis: A Comprehensive Review.Dysphagia · 2025Review
- 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
- Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?Computer methods and programs in biomedicine · 2022Article
- Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study.Journal of Korean medical science · 2022Article
- The effect of time on the automated detection of the pharyngeal phase in videofluoroscopic swallowing studies.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Hyoid Bone Tracking in a Videofluoroscopic Swallowing Study Using a Deep-Learning-Based Segmentation Network.Diagnostics (Basel, Switzerland) · 2021Article
- Automatic Pharyngeal Phase Recognition in Untrimmed Videofluoroscopic Swallowing Study Using Transfer Learning with Deep Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2021Article
- Segmentation of cervical intervertebral disks in videofluorography by CNN, multi-channelization and feature selection.International journal of computer assisted radiology and surgery · 2020Article
- Unsupervised Action Proposals Using Support Vector Classifiers for Online Video Processing.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
Videofluoroscopic swallowing study (VFSS) is a standard diagnostic tool for dysphagia. To detect the presence of aspiration during a swallow, a manual search is commonly used to mark the time intervals of the pharyngeal phase on the corresponding VFSS image. In this study, we present a novel approach that uses 3D convolutional networks to detect the pharyngeal phase in raw VFSS videos without manual annotations. For efficient collection of training data, we propose a cascade framework which no longer requires time intervals of the swallowing process nor the manual marking of anatomical positions for detection. For video classification, we applied the inflated 3D convolutional network (I3D), one of the state-of-the-art network for action classification, as a baseline architecture. We also present a modified 3D convolutional network architecture that is derived from the baseline I3D architecture. The classification and detection performance of these two architectures were evaluated for comparison. The experimental results show that the proposed model outperformed the baseline I3D model in the condition where both models are trained with random weights. We conclude that the proposed method greatly reduces the examination time of the VFSS images with a low miss rate.
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