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DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing - A Design

Alexander Wyss, Gabriela Morgenshtern, Amanda Hirsch-Husler

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    Particle contamination in medical diagnostics threatens patient safety. DaedalusData, a visual analytics system, helps experts analyze contamination data, improving quality control and reducing patient risk.

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    Area of Science:

    • Medical Diagnostics
    • Data Visualization
    • Quality Control

    Background:

    • Particle contamination in in-vitro diagnostics consumables is a significant threat to patient safety.
    • Current quality control processes struggle with analyzing large image datasets and effective knowledge externalization.
    • Objective, data-driven decisions are crucial for risk reduction and efficient quality assessment.

    Purpose of the Study:

    • To address limitations in analyzing particle contamination data in medical diagnostics.
    • To develop and validate a visual analytics system for exploring and labeling particle contamination.
    • To create a framework for knowledge externalization to improve dataset augmentation.

    Main Methods:

    • Design study methodology to characterize the problem space and requirements.
    • Development and validation of DaedalusData, a visual analytics system.
    • User study and case study to evaluate usability and efficiency.

    Main Results:

    • DaedalusData enables efficient exploration of thousands of particle images.
    • The system supports experts in labeling large quantities of particles and externalizing knowledge.
    • High usability and effectiveness in generating overviews and augmenting datasets were demonstrated.

    Conclusions:

    • DaedalusData significantly improves the analysis of particle contamination in medical diagnostics.
    • Human knowledge externalization is a viable strategy for dataset augmentation in this domain.
    • The study provides insights into the scalability and practical trade-offs of this approach.