Artificial Intelligence in the Interpretation of Videofluoroscopic Swallow Studies: Implications and Advances for Speech–Language Pathologists

Article


Girardi, Anna M., Cardell, Elizabeth A. and Bird, Stephen P.. 2023. "Artificial Intelligence in the Interpretation of Videofluoroscopic Swallow Studies: Implications and Advances for Speech–Language Pathologists ." Big Data and Cognitive Computing. 7 (4). https://doi.org/10.3390/bdcc7040178
Article Title

Artificial Intelligence in the Interpretation of Videofluoroscopic Swallow Studies: Implications and Advances for Speech–Language Pathologists

ERA Journal ID211865
Article CategoryArticle
AuthorsGirardi, Anna M., Cardell, Elizabeth A. and Bird, Stephen P.
Journal TitleBig Data and Cognitive Computing
Journal Citation7 (4)
Article Number178
Number of Pages13
Year2023
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2504-2289
Digital Object Identifier (DOI)https://doi.org/10.3390/bdcc7040178
Web Address (URL)https://www.mdpi.com/2504-2289/7/4/178
Abstract

Radiological imaging is an essential component of a swallowing assessment. Artificial intelligence (AI), and especially deep learning (DL) models, have enhanced the efficiency and efficacy through which imaging is interpreted, and subsequently have important implications for swallow diagnostics and intervention planning. However, the application of AI for the interpretation of videofluoroscopic swallowing studies (VFSS) is still emerging. This review showcases recent literature in the use of AI to interpret VFSS and highlights clinical implications for speech pathologists (SPs). With a surge in AI research, there have been advances made in dysphagia assessment. Several studies have demonstrated successful implementation of DL algorithms to analyze VFSS. Notably, convolutional neural networks (CNNs) have been used to detect pertinent aspects of the swallowing process with high levels of precision. DL algorithms have the potential to streamline VFSS interpretation, improve efficiency and accuracy, and enable precise interpretation of instrumental dysphagia evaluation, which is especially advantageous when access to skilled clinicians is not ubiquitous. By enhancing precision, speed, and depth of VFSS interpretation, SPs can obtain a more comprehensive understanding of swallow physiology and deliver targeted and timely intervention that is tailored towards the individual. This has practical applications for both clinical practice and dysphagia research. As this research area grows and AI technologies progress, the application of DL in the field of VFSS interpretation is clinically beneficial and has the potential to transform dysphagia assessment and management. With broader validation and inter-disciplinary collaborations, AI-augmented VFSS interpretation is likely to transform swallow evaluation and ultimately improve outcomes for individuals with dysphagia.

Keywordsspeech pathology; dysphagia; artificial intelligence; videofluoroscopic swallowing study; deep learning; videofluoroscopic swallowing study; deep learning; imaging; machine learning
Article Publishing Charge (APC) FundingOther
ANZSRC Field of Research 20204602. Artificial intelligence
4201. Allied health and rehabilitation science
Byline AffiliationsSchool of Health and Medical Sciences
Centre for Health Research
Griffith University
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