Eurographics Digital Library

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Recent Submissions

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The MoBa Pregnancy and Child Development Dashboard: A Design Study
(The Eurographics Association, 2024) Ziman, Roxanne; Budich, Beatrice; Vaudel, Marc; Garrison, Laura; Garrison, Laura; Jönsson, Daniel
Visual analytics dashboards enable exploration of complex medical and genetic data to uncover underlying patterns and possible relationships between conditions and outcomes. In this interdisciplinary design study, we present a characterization of the domain and expert tasks for the exploratory analysis for a rare maternal disease in the context of the longitudinal Norwegian Mother, Father, and Child (MoBa) Cohort Study. We furthermore present a novel prototype dashboard, developed through an iterative design process and using the Python-based Streamlit App [TTK18] and Vega-Altair [VGH*18] visualization library, to allow domain experts (e.g., bioinformaticians, clinicians, statisticians) to explore possible correlations between women's health during pregnancy and child development outcomes. In conclusion, we reflect on several challenges and research opportunities for not only furthering this approach, but in visualization more broadly for large, complex, and sensitive patient datasets to support clinical research.
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Leaving the Lab Setting: What We Can Learn About the Perception of Narrative Medical Visualizations from YouTube Comments
(The Eurographics Association, 2024) Mittenentzwei, Sarah; Murad, Danish; Preim, Bernhard; Meuschke, Monique; Garrison, Laura; Jönsson, Daniel
The general public is highly interested in medical information, particularly educational media about diseases, healthy biological processes such as pregnancy, and surgical procedures. Efforts to develop educational materials using data-driven approaches like narrative visualization exist, but studies are often performed in lab settings. Since there are few public sources for visualizations of medical image data, YouTube videos, which often contain 3D medical visualizations, are an important reference. We aim to better understand the user base of these videos. Therefore, we curated a dataset of 76 videos featuring medical 3D visualizations. We analyzed 14,550 comments across all videos using manual review and machine learning techniques, including natural language processing for sentiment and emotion analysis of user comments. While few comments directly link visual attributes or design choices to user sentiment, insights into users' motivation and opinions of specific design choices have emerged.
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Why, What, and How to Communicate Health Information Visually: Reflections on the Design Process of Narrative Medical Visualization
(The Eurographics Association, 2024) Mittenentzwei, Sarah; Preim, Bernhard; Meuschke, Monique; Garrison, Laura; Jönsson, Daniel
Narrative visualization is an effective technique to convey information to a lay audience in an engaging, memorable, and persuasive manner. In the medical domain, we experienced that narrative medical visualizations meet high interest from clinicians and epidemiologists as storytelling is a promising approach to conveying complex medical topics in the context of patient education and public health by utilizing medical data. These endeavors from the computer science domain are mirrored by the interdisciplinary research topic of health communication. With this work, we reflect on our past experiences by (1) showing where narrative medical visualization is applicable to solve problems clinicians face in their work, (2) summarizing all findings within a story design process, describing the key points in creating a story and how they relate to each other, and (3) highlighting parallels and insights from health communication research that can improve future narrative medical visualizations. In doing so, we aim to provide the research community with a toolkit to support the design of narrative medical visualizations.
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CardioCoLab: Collaborative Learning of Embryonic Heart Anatomy in Mixed Reality
(The Eurographics Association, 2024) Schott, Danny; Heinrich, Florian; Kunz, Matthias; Mandel, Jonas; Albrecht, Anne; Braun-Dullaeus, Rüdiger; Hansen, Christian; Garrison, Laura; Jönsson, Daniel
The complexity of embryonic heart development presents significant challenges for medical education, particularly in illustrating dynamic morphological changes over short time periods. Traditional teaching methods, such as 2D textbook illustrations and static models, are often insufficient for conveying these intricate processes. To address this gap, we developed a multi-user Mixed Reality (MR) system designed to enhance collaborative learning and interaction with virtual heart models. Building on previous research, we identified the needs of both students and teachers, implementing various interaction and visualization features iteratively. An evaluation with teachers and students (N = 12) demonstrated the system's effectiveness in improving engagement and understanding of embryonic heart development. The study highlights the potential of MR in medical seminar settings as a valuable addition to medical education by enhancing traditional learning methods.
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VISPER - Visualization System for Interactions between Proteins and Drugs for Exploratory Research
(The Eurographics Association, 2024) Dehncke, Daniel; Fiebach, Vinzenz; Kinzel, Lennart; Baumann, Knut; Kacprowski, Tim; Garrison, Laura; Jönsson, Daniel
VISPER is a web-based application that enables users to interactively explore and analyze drug-protein associations. Its uniqueness lies in the dataset for which it has been specifically designed. Until now, most biomarkers for cancer vulnerabilities have primarily relied on genomic and transcriptomic measurements. A recently published study created a comprehensive pan-cancer proteomic map of human cancer cell lines, involving the application of 625 drugs to these cell lines. From these data, proteomic responses to the drug treatment across different cell lines can be derived, providing an extensive resource for a better understanding of drug mechanisms. To facilitate the analysis of this extensive dataset, we developed VISPER, a visualization tool specifically tailored to explore the ProCan dataset, enabling easy exploration of the relationships between proteins, drugs, and cell lines through a network graph representation. The graphical representation is complemented by a wide range of filter options, different representations, and integration of existing online databases for improved biological classification. Furthermore, the web application provides a clear overview of the similarity of drugs based on their protein associations. VISPER thus represents a promising addition to established systems biology software tools. Availability and implementation: VISPER is available open-source on GitHub (https://github.com/scibiome/VISPER) or as a Docker image (https://hub.docker.com/r/thegoldenphoenix/VISPER).