ArticleComputer methods and programs in biomedicine2022
Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?
Article in Computer methods and programs in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 16 citations in OpenAlex.
- 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
- Leveraging Ensemble on Self-Supervised Techniques to Enhance Model Performance: A Regime Analysis.Journal of imaging informatics in medicine · 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
- Unsupervised Segmentation of Bolus and Residue in Videofluoroscopy Swallowing Studies.Journal of imaging · 2025Article
- Article
- Application of deep learning technology for temporal analysis of videofluoroscopic swallowing studies.Scientific reports · 2023Article
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3 authors at 2 institutions in 2 countries.
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Abstract
BACKGROUND AND
objectiveThe videofluoroscopic swallowing study (VFSS) is a gold-standard imaging technique for assessing swallowing, but analysis and rating of VFSS recordings is time consuming and requires specialized training and expertise. Researchers have recently demonstrated that it is possible to automatically detect the pharyngeal phase of swallowing and to localize the bolus in VFSS recordings via computer vision approaches, fostering the development of novel techniques for automatic VFSS analysis. However, training of algorithms to perform these tasks requires large amounts of annotated data that are seldom available. In this paper, we demonstrate that the challenges of pharyngeal phase detection and bolus localization can be solved together using a single approach.
methodsWe propose a deep-learning framework that jointly tackles pharyngeal phase detection and bolus localization in a weakly-supervised manner, requiring only the initial and final frames of the pharyngeal phase as ground truth annotations for the training. Our approach stems from the observation that bolus presence in the pharynx is the most prominent visual feature upon which to infer whether individual VFSS frames belong to the pharyngeal phase. We conducted extensive experiments with multiple convolutional neural networks (CNNs) on a dataset of 1245 bolus-level clips from 59 healthy subjects.
resultsWe demonstrated that the pharyngeal phase can be detected with an F1-score higher than 0.9. Moreover, by processing the class activation maps of the CNNs, we were able to localize the bolus with promising results, obtaining correlations with ground truth trajectories higher than 0.9, without any manual annotations of bolus location used for training purposes.
conclusionsOnce validated on a larger sample of participants with swallowing disorders, our framework will pave the way for the development of intelligent tools for VFSS analysis to support clinicians in swallowing assessment.
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