Automated assessment of hyoid movement during normal swallow using ultrasound

Abstract

Background The potential for using ultrasound by speech and language therapists (SLTs) as an adjunct clinical tool to assess swallowing function has received increased attention during the COVID-19 pandemic, with a recent review highlighting the need for further research on normative data, objective measurement, elicitation protocol and training. The dynamic movement of the hyoid, visible in ultrasound, is crucial in facilitating bolus transition and protection of the airway during a swallow and has shown promise as a biomarker of swallowing function.

Aims To examine the kinematics of the hyoid during a swallow using ultrasound imaging and to relate the patterns to the different stages of a normal swallow. To evaluate the accuracy and robustness of two different automatic hyoid tracking methods relative to manual hyoid position estimation.

Methods & Procedures Ultrasound data recorded from 15 healthy participants swallowing a 10 ml water bolus delivered by cup or spoon were analysed. The movement of the hyoid was tracked using manually marked frame-to-frame positions, automated hyoid shadow tracking and deep neural net (DNN) tracking. Hyoid displacement along the horizontal image axis (HxD) was charted throughout a swallow, and the maximum horizontal displacement (HxD max) and maximum hyoid velocity (HxV max) along the same axis were automatically calculated.

Outcomes & Results The HxD and HxV of 10 ml swallows are similar to values reported in the literature. The trajectory of the hyoid movement and its location at significant swallow event time points showed increased hyoid displacement towards the peak of the swallow. Using an interclass correlation coefficient, HxD max and HxV max values derived from the DNN tracker and shadow tracker are shown to be in high agreement and moderate agreement, respectively, when compared with values derived from manual tracking.

Conclusions & Implications The similarity of the hyoid tracking results using ultrasound to previous reports based on different instrumental tools supports the possibility of using hyoid movement as a measure of swallowing function in ultrasound. The use of machine learning to automatically track the hyoid movement potentially provides a reliable and efficient way to quantify swallowing function. These findings contribute towards improving the clinical utility of ultrasound as a swallowing assessment tool. Further research on both normative and clinical populations is needed to validate hyoid movement metrics as a means of differentiating normal and abnormal swallows and to verify the reliability of automatic tracking.