Resumen
Radiological imaging is an essential component of a swallowing assessment. Artificial intelligence (AI), especially deep learning (DL) models, has enhanced the efficiency and efficacy through which imaging is interpreted, and subsequently, it has important implications for swallow diagnostics and intervention planning. However, the application of AI for the interpretation of videofluoroscopic swallow studies (VFSS) is still emerging. This review showcases the recent literature on the use of AI to interpret VFSS and highlights clinical implications for speech?language pathologists (SLPs). With a surge in AI research, there have been advances in dysphagia assessments. Several studies have demonstrated the successful implementation of DL algorithms to analyze VFSS. Notably, convolutional neural networks (CNNs), which involve training a multi-layered model to recognize specific image or video components, 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 the precise interpretation of an instrumental dysphagia evaluation, which is especially advantageous when access to skilled clinicians is not ubiquitous. By enhancing the precision, speed, and depth of VFSS interpretation, SLPs can obtain a more comprehensive understanding of swallow physiology and deliver a 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 will likely transform swallow evaluations and ultimately improve outcomes for individuals with dysphagia. However, despite AI?s potential to streamline imaging interpretation, practitioners still need to consider the challenges and limitations of AI implementation, including the need for large training datasets, interpretability and adaptability issues, and the potential for bias.