Recent advancements and future directions in automatic swallowing analysis via videofluoroscopy: A review

Abstract

Videofluoroscopic swallowing studies (VFSS) capture the complex anatomy and physiology contributing to bolus transport and airway protection during swallowing. While clinical assessment of VFSS can be affected by evaluators subjectivity and variability in evaluation protocols, many efforts have been dedicated to developing methods to ensure consistent measures and reliable analyses of swallowing physiology using advanced computer-assisted methods. Latest advances in computer vision, pattern recognition, and deep learning technologies provide new paradigms to explore and extract information from VFSS recordings. The literature search was conducted on four bibliographic databases with exclusive focus on automatic videofluoroscopic analyses. We identified 46 studies that employ state-of-the-art image processing techniques to solve VFSS analytical tasks including anatomical structure detection, bolus contrast segmentation, and kinematic event recognition. Advanced computer vision and deep learning techniques have enabled fully automatic swallowing analysis and abnormality detection, resulting in improved accuracy and unprecedented efficiency in swallowing assessment. By establishing this review of image processing techniques applied to automatic swallowing analysis, we intend to demonstrate the current challenges in VFSS analyses and provide insight into future directions in developing more accurate and clinically explainable algorithms.