Abstract
Motion analysis of the hyoid bone via videofluoroscopic study has been used in clinical research, but the classical manual tracking method is generally labor intensive and time consuming. Although some automatic tracking methods have been developed, masked points could not be tracked and smoothing and segmentation, which are necessary for functional motion analysis prior to registration, were not provided by the previous software. We developed software to track the hyoid bone motion semi-automatically. It works even in the situation where the hyoid bone is masked by the mandible and has been validated in dysphagia patients with stroke. In addition, we added the function of semi-automatic smoothing and segmentation. A total of 30 patients’ data were used to develop the software, and data collected from 17 patients were used for validation, of which the trajectories of 8 patients were partly masked. Pearson correlation coefficients between the manual and automatic tracking are high and statistically significant (0.942 to 0.991, P-value<0.0001). Relative errors between automatic tracking and manual tracking in terms of the x-axis, y-axis and 2D range of hyoid bone excursion range from 3.3% to 9.2%. We also developed an automatic method to segment each hyoid bone trajectory into four phases (elevation phase, anterior movement phase, descending phase and returning phase). The semi-automatic hyoid bone tracking from VFSS data by our software is valid compared to the conventional manual tracking method. In addition, the ability of automatic indication to switch the automatic mode to manual mode in extreme cases and calibration without attaching the radiopaque object is convenient and useful for users. Semi-automatic smoothing and segmentation provide further information for functional motion analysis which is beneficial to further statistical analysis such as functional classification and prognostication for dysphagia. Therefore, this software could provide the researchers in the field of dysphagia with a convenient, useful, and all-in-one platform for analyzing the hyoid bone motion. Further development of our method to track the other swallowing related structures or objects such as epiglottis and bolus and to carry out the 2D curve registration may be needed for a more comprehensive functional data analysis for dysphagia with big data.
Highlights
Oropharyngeal dysphagia is caused by various diseases such as stroke, Parkinson’s disease, neuromuscular diseases, and head and neck cancer[1]
Some clinicians or researchers are using temporal parameters or kinematic parameters from motion analysis to overcome the qualitative nature of Videofluoroscopic swallow study (VFSS) and to gain more data to classify the dysphagia, to predict the prognosis or to assess the treatment effect[5,6,7,8]
Both displacement and velocity of the hyoid bone excursion are associated with swallowing function and dysphagia; the maximum excursion and peak velocity of the hyoid bone motion are associated with bolus volume[14], the hyoid bone anterior displacement is reduced in patients with myopathy and irradiated nasopharyngeal carcinoma [5, 15] and laryngeal elevation velocity was an independent predictor of aspiration in patients with acute ischemic stroke [16]
Summary
Oropharyngeal dysphagia is caused by various diseases such as stroke, Parkinson’s disease, neuromuscular diseases, and head and neck cancer[1]. The classical manual tracking methods are labour intensive and impractical in real clinical practices[17, 18] To overcome this limitation, researchers have tried to develop a software to track the hyoid bone and to get the trajectory automatically. Patrick et al [19] have reported their computer-assisted assessment of hyoid bone motion and found a high correlation between automatic tracking and manual tracking This software can reduce the burdens for VFSS motion analysis and make further quantitative analysis practically possible. The efficiency and accuracy of the automatic or semi-automatic process from hyoid bone tracking and smoothing to segmentation enables the motion analysis of VFSS to have a potential wide use in clinical practice and research.
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