Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education.

The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings.

Purpose: To provide an overview of important factors to consider when purchasing radiology artificial intelligence (AI) software and current software offerings by type, subspecialty, and modality.

Materials And Methods: Important factors for consideration when purchasing AI software, including key decision makers, data ownership and privacy, cost structures, performance indicators, and potential return on investment are described. For the market overview, a list of radiology AI companies was aggregated from the Radiological Society of North America and the Society for Imaging Informatics in Medicine conferences (November 2016-June 2019), then narrowed to companies using deep learning for imaging analysis and diagnosis.

Pilot Study of Quantitative Methods for Differentiating Pharyngeal Swallowing Mechanics by Dysphagia Etiology.

Quantitative analysis of modified barium swallow (MBS) imaging is useful to determine the impact of various disease states on pharyngeal swallowing mechanics. In this retrospective proof of concept study, kinematic analysis and computational analysis of swallowing mechanics (CASM) were used to demonstrate how these methods differentiate swallowing dysfunction by dysphagia etiology. Ten subjects were randomly selected from four cohorts of dysphagic patients including COPD, head and neck cancer (HNC), motor neuron disease, and stroke.

Functional Modules of Pharyngeal Swallowing Mechanics.

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.