Deep Learning for Automatic Hyoid Tracking in Videofluoroscopic Swallow Studies

The hyoid bone excursion is one of the most important gauges of larynx elevation in swallowing, contributing to airway protection and bolus passage into the esophagus. However, the implications of various parameters of hyoid bone excursion, such as the horizontal or vertical displacement and velocity, remain elusive and raise the need for a tool providing automatic kinematics analysis. Several conventional and deep learning-based models have been applied automatically to track the hyoid bone, but previous methods either require partial manual localization or do not transform the trajectory by anatomic axis. This work describes a convolutional neural network-based algorithm featuring fully automatic hyoid bone localization and tracking and spine axis determination. The algorithm automatically estimates the hyoid bone trajectory and calculates several physical quantities, including the average velocity and displacement in horizontal or vertical anatomic axis. The model was trained in a dataset of 365 videos of videofluoroscopic swallowing from 189 patients in a tertiary medical center and tested using 44 videos from 44 patients with different dysphagia etiologies. The algorithm showed high detection rates for the hyoid bone. The results showed excellent inter-rater reliability for hyoid bone detection, good-to-excellent inter-rater reliability for calculating the maximal displacement and the average velocity of the hyoid bone in horizontal or vertical directions, and moderate-to-good reliability in calculating the average velocity in horizontal direction. The proposed algorithm allows for complete automatic kinematic analysis of hyoid bone excursion, providing a versatile tool with high potential for clinical applications.