ArticleScientific reports2018
Automatic hyoid bone detection in fluoroscopic images using deep learning.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Construction and Application of a Quantitative Assessment Model for Swallowing Function Based on Cross-task Fusion and Temporal Context Aggregation Module.Journal of imaging informatics in medicine · 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
- 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
- Novel Videographic-Free Framework for Tracking Anatomical Structures Using Swallowing Accelerometer Signals and Multi-task Transformers.Journal of healthcare informatics research · 2025Article
- Automated 3D segmentation of the hyoid bone in CBCT using nnU-Net v2: a retrospective study on model performance and potential clinical utility.BMC medical imaging · 2025Article
- Modified Tracheal Traction Exercise Reduces the Incidence of Dysphagia in Patients with Multilevel Anterior Cervical Discectomy and Fusion.Orthopaedic surgery · 2024Article
- 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
- 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
- Article
- Temporal Sequence of Laryngeal Vestibule Closure and Reopening is Associated With Airway Protection.The Laryngoscope · 2023Article
- Quantitative approach to analyze hyoid bone movement during swallowing by ultrasound: an integrative review.CoDAS · 2023Review
- AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome.Sensors (Basel, Switzerland) · 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
- Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study.Journal of Korean medical science · 2022Article
- Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning.Medical image analysis · 2021Article
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3 authors.
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Abstract
The displacement of the hyoid bone is one of the key components evaluated in the swallow study, as its motion during swallowing is related to overall swallowing integrity. In daily research settings, experts visually detect the hyoid bone in the video frames and manually plot hyoid bone position frame by frame. This study aims to develop an automatic method to localize the location of the hyoid bone in the video sequence. To automatically detect the location of the hyoid bone in a frame, we proposed a single shot multibox detector, a deep convolutional neural network, which is employed to detect and classify the location of the hyoid bone. We also evaluated the performance of two other state-of-art detection methods for comparison. The experimental results clearly showed that the single shot multibox detector can detect the hyoid bone with an average precision of 89.14% and outperform other auto-detection algorithms. We conclude that this automatic hyoid bone tracking system is accurate enough to be widely applied as a pre-processing step for image processing in dysphagia research, as well as a promising development that may be useful in the diagnosis of dysphagia.
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