ArticleDiagnostics (Basel, Switzerland)2021
Hyoid Bone Tracking in a Videofluoroscopic Swallowing Study Using a Deep-Learning-Based Segmentation Network.
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 13 papers.
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Who cites it
13 citing papers in PubMed.
- Assessment of Hyoid Bone Movement by Simultaneous Ultrasound and Videofluoroscopic Imaging.Dysphagia · 2026Article
- Automated measurement of hair density and projected apparent width via deep learning-based phototrichogram analysis.International journal of cosmetic science · 2026Article
- Leveraging Ensemble on Self-Supervised Techniques to Enhance Model Performance: A Regime Analysis.Journal of imaging informatics in medicine · 2026Article
- Advancing modified barium swallow pre-sorting with deep learning: a new paradigm for the first step analysis in X-ray swallowing study.International journal of computer assisted radiology and surgery · 2026Article
- 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
- Novel Videographic-Free Framework for Tracking Anatomical Structures Using Swallowing Accelerometer Signals and Multi-task Transformers.Journal of healthcare informatics research · 2025Article
- Hyoid Bone Velocity and Distance during the Forward Phase Correlate with Pyriform Sinus Residue: A Retrospective Case Series.Progress in rehabilitation medicine · 2025Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Optimizing Modified Barium Swallow Exam Workflow: Automating Pre-Analysis Video Sorting in Swallowing Function Assessment.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Interactive segmentation with curve-based template deformation for spatiotemporal computed tomography of swallowing motion.PloS one · 2024Article
- A Missing Voice: The Lingual Complex and Osteopathic Manual Medicine in the Context of Five Osteopathic Models.Cureus · 2021Review
- Diagnostic value of a deep learning-based hyoid bone tracking model for aspiration in patients with post-stroke dysphagia.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Kinematic analysis of the hyoid bone in a videofluorosopic swallowing study (VFSS) is important for assessing dysphagia. However, calibrating the hyoid bone movement is time-consuming, and its reliability shows wide variation. Computer-assisted analysis has been studied to improve the efficiency and accuracy of hyoid bone identification and tracking, but its performance is limited. In this study, we aimed to design a robust network that can track hyoid bone movement automatically without human intervention. Using 69,389 frames from 197 VFSS files as the data set, a deep learning model for detection and trajectory prediction was constructed and trained by the BiFPN-U-Net(T) network. The present model showed improved performance when compared with the previous models: an area under the curve (AUC) of 0.998 for pixelwise accuracy, an accuracy of object detection of 99.5%, and a Dice similarity of 90.9%. The bounding box detection performance for the hyoid bone and reference objects was superior to that of other models, with a mean average precision of 95.9%. The estimation of the distance of hyoid bone movement also showed higher accuracy. The deep learning model proposed in this study could be used to detect and track the hyoid bone more efficiently and accurately in VFSS analysis.
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