ArticleDiagnostics (Basel, Switzerland)2021
Automatic Pharyngeal Phase Recognition in Untrimmed Videofluoroscopic Swallowing Study Using Transfer Learning with Deep Convolutional Neural Networks.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 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
- Automated detection of chewing movements in videofluoroscopic swallowing studies using deep learning for landmark detection and motion analysis.Computers in biology and medicine · 2026Article
- An intelligent quantitative analysis software for videofluoroscopic swallowing study in patients with dysphagia.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence tools for the assessment and management of dysphagia: protocol for a scoping review.BMJ open · 2025Article
- Artificial Intelligence in Videofluoroscopy Swallow Study Analysis: A Comprehensive Review.Dysphagia · 2025Review
- Article
- Application of deep learning technology for temporal analysis of videofluoroscopic swallowing studies.Scientific reports · 2023Article
- Explainability of deep learning models in medical video analysis: a survey.PeerJ. Computer science · 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
- 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
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4 authors.
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Abstract
backgroundVideo fluoroscopic swallowing study (VFSS) is considered as the gold standard diagnostic tool for evaluating dysphagia. However, it is time consuming and labor intensive for the clinician to manually search the recorded long video image frame by frame to identify the instantaneous swallowing abnormality in VFSS images. Therefore, this study aims to present a deep leaning-based approach using transfer learning with a convolutional neural network (CNN) that automatically annotates pharyngeal phase frames in untrimmed VFSS videos such that frames need not be searched manually.
methodsTo determine whether the image frame in the VFSS video is in the pharyngeal phase, a single-frame baseline architecture based the deep CNN framework is used and a transfer learning technique with fine-tuning is applied.
resultsCompared with all experimental CNN models, that fine-tuned with two blocks of the VGG-16 (VGG16-FT5) model achieved the highest performance in terms of recognizing the frame of pharyngeal phase, that is, the accuracy of 93.20 (±1.25)%, sensitivity of 84.57 (±5.19)%, specificity of 94.36 (±1.21)%, AUC of 0.8947 (±0.0269) and Kappa of 0.7093 (±0.0488).
conclusionsUsing appropriate and fine-tuning techniques and explainable deep learning techniques such as grad CAM, this study shows that the proposed single-frame-baseline-architecture-based deep CNN framework can yield high performances in the full automation of VFSS video analysis.
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