Tracking Hyoid Bone Displacement During Swallowing Without Videofluoroscopy Using Machine Learning of Vibratory Signals

Abstract

Identifying physiological impairments of swallowing is essential for determining accurate diagnosis and appropriate treatment for patients with dysphagia. The hyoid bone is an anatomical landmark commonly monitored during analysis of videofluoroscopic swallow studies (VFSSs). Its displacement is predictive of penetration/aspiration and is associated with other swallow kinematic events. However, VFSSs are not always readily available/feasible and expose patients to radiation. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from a microphone and tri-axial accelerometer, is under investigation as a non-invasive dysphagia screening method and potential adjunct to VFSS when it is unavailable or not feasible. We investigated the ability of HRCA to independently track hyoid bone displacement during swallowing with similar accuracy to VFSS, by analyzing vibratory signals from a tri-axial accelerometer using machine learning techniques. We hypothesized HRCA would track hyoid bone displacement with a high degree of accuracy compared to humans. Trained judges completed frame-by-frame analysis of hyoid bone displacement on 400 swallows from 114 patients and 48 swallows from 16 age-matched healthy adults. Extracted features from vibratory signals were used to train the predictive algorithm to generate a bounding box surrounding the hyoid body on each frame. A metric of relative overlapped percentage (ROP) compared human and machine ratings. The mean ROP for all swallows analyzed was 50.75%, indicating > 50% of the bounding box containing the hyoid bone was accurately predicted in every frame. This provides evidence of the feasibility of accurate, automated hyoid bone displacement tracking using HRCA signals without use of VFSS images.

Keywords: Cervical auscultation; Deglutition; Deglutition disorders; Dysphagia; Hyoid bone; Machine learning; Swallow screening; Videofluoroscopy.

Fig. 1

This shows the placement of the non-invasive neck sensors on the anterior laryngeal framework and the extraction of the acoustic and vibratory signals for the machine learning algorithm to track hyoid bone displacement

Fig. 2

a This shows the tracking of the hyoid bone over a period of time, b the ROP of the human-labeled and SRNN-predicted bounding boxes, c the dimensions of the bounding box, d and the overall hyoid bone displacement over time

Fig. 3

a This shows the ROP of the human-labeled and SRNN-predicted bounding boxes across the ten groups of data and b two visual examples of the ROP of the human-labeled and SRNN-predicted bounding boxes