ArticleOral radiology2023
Detection of aspiration from images of a videofluoroscopic swallowing study adopting deep learning.
Article in Oral radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 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
- Artificial intelligence tools for the assessment and management of dysphagia: a scoping review.BMJ open · 2026Article
- Multi-Center Validation of Artificial Intelligence-Based Video Analysis Platform for Automatic Evaluation of Swallowing Disorders.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning-based video analysis for automatically detecting penetration and aspiration in videofluoroscopic swallowing study.Scientific reports · 2025Article
- Deep learning and artificial intelligence in dental diagnostic imaging.The Japanese dental science review · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesA videofluoroscopic swallowing study (VFSS) is conducted to detect aspiration. However, aspiration occurs within a short time and is difficult to detect. If deep learning can detect aspirations with high accuracy, clinicians can focus on the diagnosis of the detected aspirations. Whether VFSS aspirations can be classified using rapid-prototyping deep-learning tools was studied.
methodsVFSS videos were separated into individual image frames. A region of interest was defined on the pharynx. Three convolutional neural networks (CNNs), namely a Simple-Layer CNN, Multiple-Layer CNN, and Modified LeNet, were designed for the classification. The performance results of the CNNs were compared in terms of the areas under their receiver-operating characteristic curves (AUCs).
resultsA total of 18,333 images obtained through data augmentation were selected for the evaluation. The different CNNs yielded sensitivities of 78.8%-87.6%, specificities of 91.9%-98.1%, and overall accuracies of 85.8%-91.7%. The AUC of 0.974 obtained for the Simple-Layer CNN and Modified LeNet was significantly higher than that obtained for the Multiple-Layer CNN (AUC of 0.936) (p < 0.001).
conclusionsThe results of this study show that deep learning has potential for detecting aspiration with high accuracy.
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