ArticleDysphagia2017
A Supporting Platform for Semi-Automatic Hyoid Bone Tracking and Parameter Extraction from Videofluoroscopic Images for the Diagnosis of Dysphagia Patients.
Article in Dysphagia, 2017. 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, 19 citations in OpenAlex.
- Kinematic mechanism of the rehabilitative effect of 4-channel NMES: post-hoc analysis of a prospective randomized controlled study.Scientific reports · 2023Trial
- Clinical effectiveness of the sequential 4-channel NMES compared with that of the conventional 2-channel NMES for the treatment of dysphagia in a prospective double-blind randomized controlled study.Journal of neuroengineering and rehabilitation · 2021Trial
- An intelligent quantitative analysis software for videofluoroscopic swallowing study in patients with dysphagia.Quantitative imaging in medicine and surgery · 2025Article
- Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.Auris, nasus, larynx · 2025Review
- Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?Computer methods and programs in biomedicine · 2022Article
- Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning.Medical image analysis · 2021Article
- Hyoid Bone Tracking in a Videofluoroscopic Swallowing Study Using a Deep-Learning-Based Segmentation Network.Diagnostics (Basel, Switzerland) · 2021Article
- Automatic Hyoid Bone Tracking in Real-Time Ultrasound Swallowing Videos Using Deep Learning Based and Correlation Filter Based Trackers.Sensors (Basel, Switzerland) · 2021Article
- Machine learning analysis to automatically measure response time of pharyngeal swallowing reflex in videofluoroscopic swallowing study.Scientific reports · 2020Article
- Application of Automatic Kinematic Analysis Program for the Evaluation of Dysphagia in ALS patients.Scientific reports · 2019Article
- Automatic hyoid bone detection in fluoroscopic images using deep learning.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional kinematic analysis of videofluoroscopic (VF) swallowing image, most popular for dysphagia diagnosis, requires time-consuming and repetitive manual extraction of diagnostic information from multiple images representing one swallowing period, which results in a heavy work load for clinicians and excessive hospital visits for patients to receive counseling and prescriptions. In this study, a software platform was developed that can assist in the VF diagnosis of dysphagia by automatically extracting a two-dimensional moving trajectory of the hyoid bone as well as 11 temporal and kinematic parameters. Fifty VF swallowing videos containing both non-mandible-overlapped and mandible-overlapped cases from eight patients with dysphagia of various etiologies and 19 videos from ten healthy controls were utilized for performance verification. Percent errors of hyoid bone tracking were 1.7 ± 2.1% for non-overlapped images and 4.2 ± 4.8% for overlapped images. Correlation coefficients between manually extracted and automatically extracted moving trajectories of the hyoid bone were 0.986 ± 0.017 (X-axis) and 0.992 ± 0.006 (Y-axis) for non-overlapped images, and 0.988 ± 0.009 (X-axis) and 0.991 ± 0.006 (Y-axis) for overlapped images. Based on the experimental results, we believe that the proposed platform has the potential to improve the satisfaction of both clinicians and patients with dysphagia.
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