This study investigated the relationship between the three phases of hyoid bone movement and pharyngeal residue using the videofluoroscopic swallowing study (VFSS).

We retrospectively analyzed the data from 66 patients who underwent VFSS between April 2019 and December 2019. Hyoid bone movement was classified into three phases: upward, forward, and downward. We measured the velocity and distance of hyoid bone movement in each phase, as well as the pharyngeal residue after swallowing. The correlation between hyoid bone movement and the amount of pharyngeal residue was analyzed using Spearman's rank correlation coefficient. A receiver operating characteristic (ROC) analysis was performed to evaluate the presence of pyriform sinus residue.

Hyoid bone velocity and distance during the forward phase correlated with the amount of pyriform sinus residue (velocity: r=0.311, P=0.011; distance: r=0.255, P=0.0389). ROC analysis revealed that the cutoff value for hyoid bone velocity during the forward phase was 26.1 mm/s (0.846 sensitivity, 0.604 specificity) with an area under the curve of 0.717.

The velocity and distance of the hyoid bone during the forward phase were significantly related to the amount of pyriform sinus residue. In VFSS assessment, it is important to classify hyoid bone movement into three phases-upward, forward, and downward-and to calculate its velocity and distance.

Dysphagia is one of the most common toxicities following head and neck cancer (HNC) radiotherapy (RT). Videofluoroscopic Swallowing Studies (VFSS) are the gold standard for diagnosing and assessing dysphagia, but current evaluation methods are manual, subjective, and time-consuming. This study introduces a novel framework for the automated analysis of VFSS to characterize dysphagia in HNC patients.

The proposed methodology integrates three key steps: (i) a deep learning-based labeling framework, trained iteratively to identify ten regions of interest; (ii) extraction of 23 swallowing dynamic parameters, followed by comparison across diverse cohorts; and (iii) machine learning (ML) classification of the extracted parameters into four dysphagia-related impairments.

The labeling framework achieved high accuracy, with a mean error of 1.6 pixels across the ten regions of interest in an independent test dataset. Analysis of the extracted parameters revealed significant differences in swallowing dynamics between healthy individuals, HNC patients before and after RT, and patients with non-HNC-related dysphagia. The ML classifiers achieved accuracies ranging from 0.60 to 0.87 for the four dysphagia-related impairments.

Despite challenges related to dataset size and VFSS variability, our framework demonstrates substantial potential for automatically identifying ten regions of interest and four dysphagia-related impairments from VFSS. This work sets the foundation for future research aimed at refining dysphagia analysis and characterization using VFSS, particularly in the context of HNC RT.

Videofluoroscopic swallowing studies (VFSS) capture the complex anatomy and physiology contributing to bolus transport and airway protection during swallowing. While clinical assessment of VFSS can be affected by evaluators subjectivity and variability in evaluation protocols, many efforts have been dedicated to developing methods to ensure consistent measures and reliable analyses of swallowing physiology using advanced computer-assisted methods. Latest advances in computer vision, pattern recognition, and deep learning technologies provide new paradigms to explore and extract information from VFSS recordings. The literature search was conducted on four bibliographic databases with exclusive focus on automatic videofluoroscopic analyses. We identified 46 studies that employ state-of-the-art image processing techniques to solve VFSS analytical tasks including anatomical structure detection, bolus contrast segmentation, and kinematic event recognition. Advanced computer vision and deep learning techniques have enabled fully automatic swallowing analysis and abnormality detection, resulting in improved accuracy and unprecedented efficiency in swallowing assessment. By establishing this review of image processing techniques applied to automatic swallowing analysis, we intend to demonstrate the current challenges in VFSS analyses and provide insight into future directions in developing more accurate and clinically explainable algorithms.

The hyoid bone displacement and rotation are critical kinematic events of the swallowing process in the assessment of videofluoroscopic swallow studies (VFSS). However, the quantitative analysis of such events requires frame-by-frame manual annotation, which is labor-intensive and time-consuming. Our work aims to develop a method of automatically tracking hyoid bone displacement and rotation in VFSS. We proposed a full high-resolution network, a deep learning architecture, to detect the anterior and posterior of the hyoid bone to identify its location and rotation. Meanwhile, the anterior-inferior corners of the C2 and C4 vertebrae were detected simultaneously to automatically establish a new coordinate system and eliminate the effect of posture change. The proposed model was developed by 59,468 VFSS frames collected from 1488 swallowing samples, and it achieved an average landmark localization error of 2.38 pixels (around 0.5% of the image with 448 × 448 pixels) and an average angle prediction error of 0.065 radians in predicting C2-C4 and hyoid bone angles. In addition, the displacement of the hyoid bone center was automatically tracked on a frame-by-frame analysis, achieving an average mean absolute error of 2.22 pixels and 2.78 pixels in the x-axis and y-axis, respectively. The results of this study support the effectiveness and accuracy of the proposed method in detecting hyoid bone displacement and rotation. Our study provided an automatic method of analyzing hyoid bone kinematics during VFSS, which could contribute to early diagnosis and effective disease management.

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of healthcare data, including clinical NLP, medical imaging, structured Electronic Health Records (EHR), social media, bio-physiological signals, biomolecular sequences. Furthermore, which have also include the articles that used the transformer architecture for generating surgical instructions and predicting adverse outcomes after surgeries under the umbrella of critical care. Under diverse settings, these models have been used for clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. Finally, we also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.

Modified Barium Swallow (MBS) exams, performed using video-fluoroscopy, an X-ray imaging technique, are essential for assessing swallowing function. They visualize the barium bolus (contrast agent) during the swallowing process in the head and neck area, thereby providing crucial insights into the dynamics of swallowing. Typically, these exams include both diagnostic anteroposterior (AP) and lateral planes, in addition to non-diagnostic "scout" films. This study introduces a deep learning solution aimed at streamlining the pre-analysis process of MBS exams by automating the identification of video orientations and scout video clips. Our methods are trained and tested on a comprehensive dataset comprising 2,315 video clips from 172 MBS exams and 106 patients. To distinguish AP videos from lateral views, our model achieved more than 99% accuracy at the frame level and 100% at the video level. In differentiating scout from bolus swallowing tasks, the model attained a maximum accuracy of 86% at the video level. We further merged these two tasks into a multi-task learning approach further enhanced the accuracy to 91% for scout/bolus differentiation. These advancements allow clinicians to allocate more efforts to focus primarily on lateral view videos for clinically relevant measurements such as the Penetration-Aspiration Scale (PAS) and Dynamic Imaging Grade of Swallowing Toxicity (DIGEST). This image sorting is also a pre-requisite step necessary to apply deep learning solutions to full image analysis.

Bolus segmentation is crucial for the automated detection of swallowing disorders in videofluoroscopic swallowing studies (VFSS). However, it is difficult for the model to accurately segment a bolus region in a VFSS image because VFSS images are translucent, have low contrast and unclear region boundaries, and lack color information. To overcome these challenges, we propose PECI-Net, a network architecture for VFSS image analysis that combines two novel techniques: the preprocessing ensemble network (PEN) and the cascaded inference network (CIN). PEN enhances the sharpness and contrast of the VFSS image by combining multiple preprocessing algorithms in a learnable way. CIN reduces ambiguity in bolus segmentation by using context from other regions through cascaded inference. Moreover, CIN prevents undesirable side effects from unreliably segmented regions by referring to the context in an asymmetric way. In experiments, PECI-Net exhibited higher performance than four recently developed baseline models, outperforming TernausNet, the best among the baseline models, by 4.54% and the widely used UNet by 10.83%. The results of the ablation studies confirm that CIN and PEN are effective in improving bolus segmentation performance.

Hyoid bone movement is potentially related to aspiration risk in post-stroke dysphagia (PSD) patients but is difficult to assess quantitatively. This study aimed to measure the distance of hyoid bone movement more efficiently and accurately using a deep learning model and determine the clinical usefulness of the model in PSD patients.

This study included 85 patients with PSD within 6 months from onset. Patients were grouped into an aspiration group (n = 35) and a non-aspiration group (n = 50) according to the results of a videofluoroscopic swallowing study. Hyoid bone movement was tracked using a deep learning model constructed with the BiFPN-U-Net(T) architecture. The maximum distance of hyoid bone movement was measured horizontally (H max), vertically (V max), and diagonally (D max).

Compared with the non-aspiration group, the aspiration group showed significant decreases in hyoid bone movement in all directions. The area under the curve of V max was highest at 0.715 with a sensitivity of 0.680 and specificity of 0.743. The V max cutoff value for predicting aspiration risk was 1.61 cm. The success of oral feeding at the time of discharge was significantly more frequent when hyoid movement was equal to or larger than the cutoff value although no significant relationship was found between hyoid movement and other clinical characteristics.

Hyoid bone movement of PSD patients can be measured quantitatively and efficiently using a deep learning model. Deep learning model-based analysis of hyoid bone movement seems to be useful for predicting aspiration risk and the possibility of resuming oral feeding.

Videofluoroscopic swallowing study (VFSS) visualizes the swallowing movement by using X-ray fluoroscopy, which is the most widely used method for dysphagia examination. To better facilitate swallowing assessment, the temporal parameter is one of the most important indicators. However, most information of that acquire is hand-crafted and elaborated, which is time-consuming and difficult to ensure objectivity and accuracy. In this article, we propose to formulate this task as a temporal action localization task and solve it using deep neural networks. However, the action of VFSS has the following characteristics such as small motion targets, small action amplitudes, large sample variances, short duration, and variations in duration. Furthermore, all existing methods often rely on daily behaviors, which makes locating and recognizing micro-actions more challenging. To address the above issues, we first collect and annotate the VFSS micro-action dataset, which includes 847 VFSS data from 71 subjects, due to the lack of benchmarks. We then introduce a coarse-to-fine mechanism to handle the short and repeated nature of micro-actions, which can significantly enhancing micro-action localization accuracy. Moreover, we propose a Variable-Size Window Generator method, which improves the model's characterization performance and addresses the issue of different action timings, leading to further improvements in localization accuracy. The results of our experiments demonstrate the superiority of our method, with significantly improved performance (46.10% vs. 37.70%).

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.

The five osteopathic models recognized by the American Association of Colleges of Osteopathic Medicine guide clinicians in the evaluation and therapeutic choice which must be the most appropriate concerning the patient's needs. Skeletal muscles represent an important interpretation, such as screening and treatment, on which these models are based. A muscle district that is not considered by the usual osteopathic practice is the tongue. The lingual complex has numerous functions, both local and systemic; it can adapt negatively in the presence of pathology, just as it can influence the body system in a non-physiological manner if it is a source of dysfunctions. This paper, the first of its kind in the panorama of scientific literature, briefly reviews the anatomy and neurophysiology of the tongue, trying to highlight the logic and the need to insert this muscle in the context of the five osteopathic models. The clinician's goal is to restore the patient's homeostasis, and we believe that this task is more concrete if the patient is approached after understanding all the contractile districts, including the tongue.