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Related Concept Videos

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Related Experiment Video

Updated: May 1, 2026

Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
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Artificial Intelligence in Videofluoroscopy Swallow Study Analysis: A Comprehensive Review.

G Sanjeevi1, Uma Gopalakrishnan2, Rahul Krishnan Pathinarupothi2

  • 1Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India. sanjeevig1999@gmail.com.

Dysphagia
|February 17, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in analyzing Videofluoroscopic Swallowing Studies (VFSS) to improve dysphagia diagnosis. While AI aids in detecting swallowing phases and abnormalities, a fully automated tool for VFSS analysis is still under development.

Keywords:
Airway invasionArtificial intelligenceDysphagiaSegmentationSwallowing phase

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine
  • Speech-Language Pathology

Background:

  • Videofluoroscopic Swallowing Study (VFSS) is the gold standard for diagnosing dysphagia.
  • VFSS interpretation faces challenges due to human bias and variability.
  • Artificial intelligence (AI) offers potential solutions to enhance VFSS analysis.

Purpose of the Study:

  • To review current AI applications in analyzing VFSS for swallowing disorders.
  • To assess AI's role in supporting clinical decision-making for dysphagia.
  • To identify progress and limitations in AI-driven VFSS analysis.

Main Methods:

  • Comprehensive literature review of AI techniques applied to VFSS.
  • Analysis of AI's performance in specific VFSS tasks like phase detection and abnormality identification.
  • Evaluation of AI model generalizability and integration challenges.

Main Results:

  • Significant AI advancements in pharyngeal phase detection, bolus/hyoid bone segmentation, and penetration-aspiration detection.
  • AI shows potential for analyzing clinical relevance and expanding VFSS scope.
  • An end-to-end automated AI tool for VFSS analysis is not yet available.

Conclusions:

  • AI holds considerable potential to improve the objectivity and efficiency of VFSS analysis.
  • Further research is needed on dataset availability, model generalizability, and clinical integration for speech-language pathologists.
  • AI can enhance diagnostic accuracy and clinical decision support in swallowing disorder assessment.