Abstract
(1) Background: Ultrasound provides a radiation-free and portable method for assessing swallowing. Hyoid bone locations and displacements are often used as important indicators for the evaluation of swallowing disorders. However, this requires clinicians to spend a great deal of time reviewing the ultrasound images. (2) Methods: In this study, we applied tracking algorithms based on deep learning and correlation filters to detect hyoid locations in ultrasound videos collected during swallowing. Fifty videos were collected from 10 young, healthy subjects for training, evaluation, and testing of the trackers. (3) Results: The best performing deep learning algorithm, Fully-Convolutional Siamese Networks (SiamFC), proved to have reliable performance in getting accurate hyoid bone locations from each frame of the swallowing ultrasound videos. While having a real-time frame rate (175 fps) when running on an RTX 2060, SiamFC also achieved a precision of 98.9% at the threshold of 10 pixels (3.25 mm) and 80.5% at the threshold of 5 pixels (1.63 mm). The tracker’s root-mean-square error and average error were 3.9 pixels (1.27 mm) and 3.3 pixels (1.07 mm), respectively. (4) Conclusions: Our results pave the way for real-time automatic tracking of the hyoid bone in ultrasound videos for swallowing assessment.
Highlights
Swallowing problems, called dysphagia, have a prevalence of 16–23% in the general population, reaching 27% in those over 76 years of age [1]
SiamFC trained with Ultrasound Swallowing Videos (USV) gave an RMSE of 3.85 accuracy and speed
We proposed to use deep learning tracking algorithms and correlation filter tracking algorithms to automatically track the locations of the hyoid bone in swallowing clips collected using ultrasound imaging
Summary
Swallowing problems, called dysphagia, have a prevalence of 16–23% in the general population, reaching 27% in those over 76 years of age [1]. It influences 16% of 87 years or older group in the Netherlands [2], affecting up to 40% of people in permanentcare settings [3] and 50 to 75% of nursing home residents [4]. Martino et al (2005) reported that up to 37–78% of stroke patients have dysphagia [5]. Sapir et al (2008) demonstrated that 90% of Parkinson’s disease patients present with dysphagia [6]. It requires the patient to stay in a fixed position and consume barium-coated materials; X-ray videos are taken, usually on the sagittal plane
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