Deep learning-based video analysis for automatically detecting penetration and aspiration in videofluoroscopic swallowing study

Deep learning-based video analysis for automatically detecting penetration and aspiration in videofluoroscopic swallowing study

Abstract The videofluoroscopic swallowing study (VFSS) is the gold standard for diagnosing dysphagia, but its interpretation is time-consuming and requires expertise. This study developed a deep learning model for automatically detecting penetration and aspiration in VFSS and assessed its diagnostic...

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Bibliographic Details
Main Authors: Soyoung Kwak, Jeoung Kun Kim, Jun Sung Moon, Gun Woo Lee, Sungho Kim, Min Cheol Chang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Videofluoroscopic swallowing study
Dysphasia
Artificial intelligence
Deep learning
Convolutional neural network
Online Access:https://doi.org/10.1038/s41598-025-10397-0
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https://doi.org/10.1038/s41598-025-10397-0

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