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    This study introduces a novel visualization for esophageal motility disorders using high-resolution manometry. It improves diagnostic efficiency by integrating the Chicago Classification into an intuitive, data-driven visual representation.

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    Area of Science:

    • Gastroenterology
    • Medical Imaging
    • Data Visualization

    Background:

    • High-resolution manometry (HRM) is crucial for diagnosing esophageal motility disorders.
    • Current HRM visualization methods often treat swallows as individual events, overlooking aggregated metrics vital for diagnosis.
    • The Chicago Classification serves as the established standard for categorizing these disorders.

    Purpose of the Study:

    • To develop an enhanced visualization technique for esophageal motility disorders based on HRM data.
    • To create a novel decision graph representing the Chicago Classification for optimized diagnostic workflow.
    • To improve the efficiency and accuracy of diagnosing esophageal motility disorders.

    Main Methods:

    • Development of a novel decision graph for the Chicago Classification.
    • Integration of prioritized diagnostic metrics into a data-driven visualization.
    • Creation of a visualization that intuitively represents different disorders and their parameters.
    • Implementation of a user study involving medical students and a domain expert.
    • Exploration of visual signatures for patient comparison using small multiples.

    Main Results:

    • The proposed visualization effectively supports efficient and correct diagnosis of esophageal motility disorders.
    • The novel decision graph optimizes the diagnostic workflow by prioritizing key metrics.
    • User study results indicate the visualization's utility for both students and experts.
    • A method for generating visual signatures for intuitive patient comparison was developed.

    Conclusions:

    • The novel visualization enhances the diagnostic process for esophageal motility disorders identified via HRM.
    • The decision graph and prioritized metrics streamline diagnosis according to the Chicago Classification.
    • The developed visualization and patient comparison method offer significant advancements in the field.