Artificial Intelligence in the Interpretation of Videofluoroscopic Swallow Studies: Implications and Advances for Speech–Language Pathologists
Article
Article Title | Artificial Intelligence in the Interpretation of Videofluoroscopic Swallow Studies: Implications and Advances for Speech–Language Pathologists |
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ERA Journal ID | 211865 |
Article Category | Article |
Authors | Girardi, Anna M., Cardell, Elizabeth A. and Bird, Stephen P. |
Journal Title | Big Data and Cognitive Computing |
Journal Citation | 7 (4) |
Article Number | 178 |
Number of Pages | 13 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2504-2289 |
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. |
Keywords | speech pathology; dysphagia; artificial intelligence; videofluoroscopic swallowing study; deep learning; videofluoroscopic swallowing study; deep learning; imaging; machine learning |
Article Publishing Charge (APC) Funding | Other |
ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
4201. Allied health and rehabilitation science | |
Byline Affiliations | School of Health and Medical Sciences |
Centre for Health Research | |
Griffith University |
https://research.usq.edu.au/item/z0xvz/artificial-intelligence-in-the-interpretation-of-videofluoroscopic-swallow-studies-implications-and-advances-for-speech-language-pathologists
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