Objectives
The present retrospective cohort study aims to test the hypothesis that elements of swallowing mechanics including hyoid movement, laryngeal elevation, tongue base retraction, pharyngeal shortening, pharyngeal constriction, and head and neck extension can be grouped into functional modules, and that these modules are predictably altered in disease states.
Methods
Modified barium swallow video clips of a thick and a thin liquid swallow from 40 normal patients and 10 dysphagic post‐treatment oropharyngeal head‐and‐neck cancer (HNC) patients were used in this study. Coordinate locations of 12 anatomical landmarks mapping pharyngeal swallowing mechanics were tracked on every frame during the pharyngeal phase of each swallow using a custom‐made MATLAB tool. Morphometric modularity hypothesis testing was performed on these coordinate data to characterize the modular elements of swallowing function in each cohort using MorphoJ software.
Results
The elements of normal swallowing can be grouped into four functional modules including bolus propulsion, pharyngeal shortening, airway protection, and head and neck posture. Modularity in HNC patient showed an intact airway protection module but altered bolus propulsion and pharyngeal shortening modules. To cross‐validate the alteration in modules, a post hoc analysis was performed, which showed significantly increased vallecular (
P
< .04) and piriform (
P
< .05) residue but no significant change in aspiration status in the HNC cohort versus controls.
Conclusions
This study suggests that while pharyngeal swallowing mechanics is highly complex, the system is organized into functional modules, and that changes in modularity impacts swallowing performance. This approach to understanding swallowing function may help the patient care team better address swallowing difficulties.
Level of Evidence
2b
T he radiology community has had a leading role in exploring medical applications of artificial intelligence (AI), and one of the primary drivers for this is the desire for increased accuracy and efficiency in clinical care. Radiologist responsibilities extend beyond image interpretation. AI tools have the potential to improve essential tasks in the imaging value chain, from image acquisition to generating and disseminating radiology reports (1). These applications are crucial in current medical environments with increasing workloads, increasing scan complexity, and the need to decrease costs and reduce errors (2-4). AI applications related to radiologic quality, safety, and workflow improvements can be grouped by their influence on various steps in the typical radiology workflow, as follows in their approximate order of occurrence: study selection and protocoling; image acquisition; worklist prioritization; study reporting, business applications, and resident education. This qualitative review is a discussion of current research and commercial models regarding these applications within the entire imaging chain.
Methods
Studiespublished from 1980 through 2019 were retrieved nonsystematically from academic search engines including PubMed, ScienceDirect, and Google Scholar by using search terms related to each application of interest. Public legal documents were also accessed including the Medicare Physician Fee Schedule and Other Revisions to Part B, Quality Payment Program requirements, and Shared Savings Program requirements.Public news sources, such as Becker's Hospital Review, Healthcare Finance, Optum, and Healthcare IT News, and vendor lists from meetings of the Radiological Society of North America and the Society for Imaging Informatics in Medicine were used to find any commercial efforts in each space. All searches were performed by the authors, all of whom are attending radiologists or trainees with a research interest in radiology AI.
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