ArticleJournal of Korean medical science2022
Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study.
Article in Journal of Korean medical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 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
- 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
- What the "Voices" of Dysphagia Are Thinking about Its Future.International archives of otorhinolaryngology · 2026Article
- Multi-Center Validation of Artificial Intelligence-Based Video Analysis Platform for Automatic Evaluation of Swallowing Disorders.Diagnostics (Basel, Switzerland) · 2025Article
- Detecting Airway Invasion in Variable-Length Videofluoroscopic Swallowing Studies: A Vision Transformer Approach for Oropharyngeal Dysphagia.Diagnostics (Basel, Switzerland) · 2025Article
- A Machine Learning Pipeline for Automated Bolus Segmentation and Area Measurement in Swallowing Videofluoroscopy Images of an Infant Pig Model.Dysphagia · 2025Article
- Deep learning-based video analysis for automatically detecting penetration and aspiration in videofluoroscopic swallowing study.Scientific reports · 2025Article
- Construction of a deep - learning - based rehabilitation prediction model for lower-limb motor dysfunction after stroke using synchronous EEG-EMG and fMRI.Frontiers in neuroscience · 2025Article
- Deep learning algorithm for automatically measuring Cobb angle in patients with idiopathic scoliosis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Review
- Inter-rater reliability and clinical relevance of subjective and objective interpretation of videofluoroscopy findings.Laryngoscope investigative otolaryngology · 2024Article
- The Development of an Artificial Intelligence Video Analysis-Based Web Application to Diagnose Oropharyngeal Dysphagia: A Pilot Study.Brain sciences · 2024Article
- Convolutional neural network algorithm trained on lumbar spine radiographs to predict outcomes of transforaminal epidural steroid injection for lumbosacral radicular pain from spinal stenosis.Scientific reports · 2024Article
- Identification of L5 vertebra on lumbar spine radiographs using deep learning.The Journal of international medical research · 2024Article
- Post-stroke Everything.Current neurology and neuroscience reports · 2023Review
- Artificial Intelligence and the Evaluation and Treatment of Stroke.Delaware journal of public health · 2023Article
- Editorial: Machine learning in data analysis for stroke/endovascular therapy.Frontiers in neurology · 2023Article
- AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome.Sensors (Basel, Switzerland) · 2022Article
- Use of Machine Learning in Stroke Rehabilitation: A Narrative Review.Brain & NeuroRehabilitation · 2022Review
- A Retrospective Clinical Evaluation of an Artificial Intelligence Screening Method for Early Detection of STEMI in the Emergency Department.Journal of Korean medical science · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundVideofluoroscopic swallowing study (VFSS) is currently considered the gold standard to precisely diagnose and quantitatively investigate dysphagia. However, VFSS interpretation is complex and requires consideration of several factors. Therefore, considering the expected impact on dysphagia management, this study aimed to apply deep learning to detect the presence of penetration or aspiration in VFSS of patients with dysphagia automatically.
methodsThe VFSS data of 190 participants with dysphagia were collected. A total of 10 frame images from one swallowing process were selected (five high-peak images and five low-peak images) for the application of deep learning in a VFSS video of a patient with dysphagia. We applied a convolutional neural network (CNN) for deep learning using the Python programming language. For the classification of VFSS findings (normal swallowing, penetration, and aspiration), the classification was determined in both high-peak and low-peak images. Thereafter, the two classifications determined through high-peak and low-peak images were integrated into a final classification.
resultsThe area under the curve (AUC) for the validation dataset of the VFSS image for the CNN model was 0.942 for normal findings, 0.878 for penetration, and 1.000 for aspiration. The macro average AUC was 0.940 and micro average AUC was 0.961.
conclusionThis study demonstrated that deep learning algorithms, particularly the CNN, could be applied for detecting the presence of penetration and aspiration in VFSS of patients with dysphagia.
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