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A Survey of Visual Analytics for Public Health
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Preim, Bernhard; Lawonn, Kai; Benes, Bedrich and Hauser, Helwig
We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.
Implicit Modeling of Patient-Specific Aortic Dissections with Elliptic Fourier Descriptors
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Mistelbauer, Gabriel; Rössl, Christian; Bäumler, Kathrin; Preim, Bernhard; Fleischmann, Dominik; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Aortic dissection is a life-threatening vascular disease characterized by abrupt formation of a new flow channel (false lumen) within the aortic wall. Survivors of the acute phase remain at high risk for late complications, such as aneurysm formation, rupture, and death. Morphologic features of aortic dissection determine not only treatment strategies in the acute phase (surgical vs. endovascular vs. medical), but also modulate the hemodynamics in the false lumen, ultimately responsible for late complications. Accurate description of the true and false lumen, any communications across the dissection membrane separating the two lumina, and blood supply from each lumen to aortic branch vessels is critical for risk prediction. Patient-specific surface representations are also a prerequisite for hemodynamic simulations, but currently require time-consuming manual segmentation of CT data. We present an aortic dissection cross-sectional model that captures the varying aortic anatomy, allowing for reliable measurements and creation of high-quality surface representations. In contrast to the traditional spline-based cross-sectional model, we employ elliptic Fourier descriptors, which allows users to control the accuracy of the cross-sectional contour of a flow channel. We demonstrate (i) how our approach can solve the requirements for generating surface and wall representations of the flow channels, (ii) how any number of communications between flow channels can be specified in a consistent manner, and (iii) how well branches connected to the respective flow channels are handled. Finally, we discuss how our approach is a step forward to an automated generation of surface models for aortic dissections from raw 3D imaging segmentation masks.
Visual Analytics to Support Treatment Decisions in Late-Stage Melanoma Patients
(The Eurographics Association, 2023) Pereira, Calida; Niemann, Uli; Braun, Andreas; Mengoni, Miriam; Tüting, Thomas; Preim, Bernhard; Meuschke, Monique; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
We present a visual analytics system to support treatment decisions in late-stage Melanoma patients. With the aim of improving patient outcomes, personalized treatment decisions based on individual characteristics and medical histories are crucial. The research focuses on the design and development of a visual analytics system tailored specifically for tumor boards, where multidisciplinary teams collaborate to make informed decisions. By leveraging a comprehensive database containing treatment and tumor stage progression information from over 1100 patients, the system provides healthcare professionals with a holistic overview and facilitates the analysis of individual cases as well as comparisons between multiple patients. The distinction between tumor board preparation systems and systems used during discussions is emphasized to ensure user-centric design and usability. Through the use of visual analytics techniques, complex relationships between treatment outcomes, temporal features, and patient-specific factors are explored, enabling clinicians to identify patterns and trends that may impact treatment decisions. The findings of this research contribute to the growing field of visual analytics in healthcare and have the potential to enhance treatment decision-making and patient care in late-stage cancer scenarios.
COMFIS - Comparative Visualization of Simulated Medical Flow Data
(The Eurographics Association, 2022) Meuschke, Monique; Voß, Samuel; Eulzer, Pepe; Janiga, Gabor; Arens, Christoph; Wickenhöfer, Ralph; Preim, Bernhard; Lawonn, Kai; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun Wu
Simulations of human blood and airflow are playing an increasing role in personalized medicine. Comparing flow data of different treatment scenarios or before and after an intervention is important to assess treatment options and success. However, existing visualization tools are either designed for the evaluation of a single data set or limit the comparison to a few partial aspects such as scalar fields defined on the vessel wall or internal flow patterns. Therefore, we present COMFIS, a system for the comparative visual analysis of two simulated medical flow data sets, e.g. before and after an intervention. We combine various visualization and interaction methods for comparing different aspects of the underlying, often time-dependent data. These include comparative views of different scalar fields defined on the vessel/mucous wall, comparative depictions of the underlying volume data, and comparisons of flow patterns. We evaluated COMFIS with CFD engineers and medical experts, who were able to efficiently find interesting data insights that help to assess treatment options.
Aneulysis - A System for Aneurysm Data Analysis
(The Eurographics Association, 2020) Meuschke, Monique; Wickenhöfer, Ralph; Preim, Bernhard; Lawonn, Kai; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
We present ANEULYSIS, a system to improve risk assessment and treatment planning of cerebral aneurysms. Aneurysm treatment must be carefully examined as there is a risk of fatal outcome during surgery. Aneurysm growth, rupture, and treatment success depend on the interplay of vascular morphology and hemodynamics. Blood flow simulations can obtain the patient-specific hemodynamics. However, analyzing the time-dependent, multi-attribute data is time-consuming and error-prone. ANEULYSIS supports the analysis and visual exploration of aneurysm data including morphological and hemodynamic attributes. Since this is an interdisciplinary process involving both physicians and fluid mechanics experts, we provide a redundancy-free management of aneurysm data sets according to a consistent structure. Major contributions are an improved analysis of morphological aspects, simultaneous evaluation of wall- and flow-related characteristics as well as multiple attributes on the vessel wall, the assessment of mechanical wall processes as well as an automatic classification of the internal flow behavior. It was designed and evaluated in collaboration with domain experts who confirmed its usefulness and clinical necessity.
Reflections on AI-Assisted Character Design for Data-Driven Medical Stories
(The Eurographics Association, 2023) Budich, Beatrice; Garrison, Laura A.; Preim, Bernhard; Meuschke, Monique; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
Data-driven storytelling has experienced significant growth in recent years to become a common practice in various application areas, including healthcare. Within the realm of medical narratives, characters play a pivotal role in connecting audiences with data and conveying complex medical information in an engaging manner that may influence positive behavioral and lifestyle changes on the part of the viewer. However, the process of designing characters that are both informative and engaging remains a challenge. In this paper, we propose an AI-assisted pipeline for character design in the context of data-driven medical stories. Our iterative pipeline blends design sensibilities with automation to reduce the time and artistic expertise needed to develop characters reflective of the underlying data, even when that data is time-oriented as in a cohort study.
An Exploration of Practice and Preferences for the Visual Communication of Biomedical Processes
(The Eurographics Association, 2021) Garrison, Laura; Meuschke, Monique; Fairman, Jennifer; Smit, Noeska N.; Preim, Bernhard; Bruckner, Stefan; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
The visual communication of biomedical processes draws from diverse techniques in both visualization and biomedical illustration. However, matching these techniques to their intended audience often relies on practice-based heuristics or narrow-scope evaluations. We present an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. Designed over a series of expert interviews and focus groups, our study focuses on common communication scenarios of five well-known biomedical processes and their standard visual representations. We framed these scenarios in a survey with participant expertise spanning from minimal to expert knowledge of a given topic. Our results show frequent overlap in abstraction preferences between expert and non-expert audiences, with similar prioritization of clarity and the ability of an asset to meet a given communication objective. We also found that some illustrative conventions are not as clear as we thought, e.g., glows have broadly ambiguous meaning, while other approaches were unexpectedly preferred, e.g., biomedical illustrations in place of data-driven visualizations. Our findings suggest numerous opportunities for the continued convergence of visualization and biomedical illustration techniques for targeted visualization design.
Transdisciplinary Visualization of Aortic Dissections
(The Eurographics Association, 2023) Mistelbauer, Gabriel; Bäumler, Kathrin; Mastrodicasa, Domenico; Hahn, Lewis D.; Pepe, Antonio; Sandfort, Veit; Hinostroza, Virginia; Ostendorf, Kai; Schroeder, Aaron; Sailer, Anna M.; Willemink, Martin J.; Walters, Shannon; Preim, Bernhard; Fleischmann, Dominik; Raidou, Renata; Kuhlen, Torsten
Aortic dissection is a life-threatening condition caused by the abrupt formation of a secondary blood flow channel within the vessel wall. Patients surviving the acute phase remain at high risk for late complications, such as aneurysm formation and aortic rupture. The timing of these complications is variable, making long-term imaging surveillance crucial for aortic growth monitoring. Morphological characteristics of the aorta, its hemodynamics, and, ultimately, risk models impact treatment strategies. Providing such a wealth of information demands expertise across a broad spectrum to understand the complex interplay of these influencing factors. We present results of our longstanding transdisciplinary efforts to confront this challenge. Our team has identified four key disciplines, each requiring specific expertise overseen by radiology: lumen segmentation and landmark detection, risk predictors and inter-observer analysis, computational fluid dynamics simulations, and visualization and modeling. In each of these disciplines, visualization supports analysis and serves as communication medium between stakeholders, including patients. For each discipline, we summarize the work performed, the related work, and the results.
Knowledge-Assisted Comparative Assessment of Breast Cancer using Dynamic Contrast-Enhanced Magnetic Resonance Imaging
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Nie, Kai; Baltzer, Pascal; Preim, Bernhard; Mistelbauer, Gabriel; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Breast perfusion data are dynamic medical image data that depict perfusion characteristics of the investigated tissue. These data consist of a series of static datasets that are acquired at different time points and aggregated into time intensity curves (TICs) for each voxel. The characteristics of these TICs provide important information about a lesion's composition, but their analysis is time-consuming due to their large number. Subsequently, these TICs are used to classify a lesion as benign or malignant. This lesion scoring is commonly done manually by physicians and may therefore be subject to bias. We propose an approach that addresses both of these problems by combining an automated lesion classification with a visual confirmatory analysis, especially for uncertain cases. Firstly, we cluster the TICs of a lesion using ordering points to identify the clustering structure (OPTICS) and then visualize these clusters. Together with their relative size, they are added to a library. We then model fuzzy inference rules by using the lesion's TIC clusters as antecedents and its score as consequent. Using a fuzzy scoring system, we can suggest a score for a new lesion. Secondly, to allow physicians to confirm the suggestion in uncertain cases, we display the TIC clusters together with their spatial distribution and allow them to compare two lesions side by side. With our knowledge-assisted comparative visual analysis, physicians can explore and classify breast lesions. The true positive prediction accuracy of our scoring system achieved 71.4% in one-fold cross-validation using 14 lesions.
Do Disease Stories need a Hero? Effects of Human Protagonists on a Narrative Visualization about Cerebral Small Vessel Disease
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Mittenentzwei, Sarah; Weiß, Veronika; Schreiber, Stefanie; Garrison, Laura A.; Bruckner, Stefan; Pfister, Malte; Preim, Bernhard; Meuschke, Monique; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
Authors use various media formats to convey disease information to a broad audience, from articles and videos to interviews or documentaries. These media often include human characters, such as patients or treating physicians, who are involved with the disease. While artistic media, such as hand-crafted illustrations and animations are used for health communication in many cases, our goal is to focus on data-driven visualizations. Over the last decade, narrative visualization has experienced increasing prominence, employing storytelling techniques to present data in an understandable way. Similar to classic storytelling formats, narrative medical visualizations may also take a human character-centered design approach. However, the impact of this form of data communication on the user is largely unexplored. This study investigates the protagonist's influence on user experience in terms of engagement, identification, self-referencing, emotional response, perceived credibility, and time spent in the story. Our experimental setup utilizes a character-driven story structure for disease stories derived from Joseph Campbell's Hero's Journey. Using this structure, we generated three conditions for a cerebral small vessel disease story that vary by their protagonist: (1) a patient, (2) a physician, and (3) a base condition with no human protagonist. These story variants formed the basis for our hypotheses on the effect of a human protagonist in disease stories, which we evaluated in an online study with 30 participants. Our findings indicate that a human protagonist exerts various influences on the story perception and that these also vary depending on the type of protagonist.

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