Eye-tracking technology has been increasingly utilized in various medical domains, yet its adoption in pathology remains limited. The lack of standardized methodologies and the complexity of analyzing whole-slide images pose significant challenges for applying eye-tracking in this domain. Existing studies often rely on proprietary and heterogeneous software solutions, reducing reproducibility and comparability of results across researchers. To address these issues, we present a novel eye-tracking study platform specifically designed for digital pathology. The proposed platform enables standardized and reproducible eye-tracking studies by providing a modular architecture that supports common eye-tracking software and hardware. It features a web-based frontend for study execution and a backend deployed via Docker, ensuring platformindependent usability while maintaining local data storage for privacy compliance and GDPR adherence. An experimental study was conducted with pathologists, trainees, and medical students to validate the applicability of the platform, as well as to demonstrate its reliable performance and ease-of-use. While the platform proved technically robust, areas for improvement remain. Future enhancements will focus on refining the user experience by integrating an improved tutorial system and posttask feedback mechanisms, incorporating gamification elements to boost participant engagement and data quality. Additionally, the next major version will introduce full support for whole-slide images, including zooming and advanced navigation features, to provide more comprehensive insights into pathologists' visual attention patterns. By providing a structured and adaptable research framework, our work represents a significant step toward standardizing eye-tracking research in pathology. The developed platform provides the foundation for more consistent study designs and reproducible findings, ultimately contributing to the advancement of digital pathology and diagnostic training methodologies.

Glioblastoma is the most common malignant brain tumor with a poor survival rate due to its high intra- and intertumor heterogeneity. The current heterogeneity determination is based on a microscopic analysis of Hematoxilyn and Eosinstained tumor slides carried out by experienced neuropathologists. There is no standardized procedure yet, to quantify heterogeneity, though. With the hypothesis that the amount of heterogeneity impacts overall survival, we aim to develop an objective method to capture heterogeneity. We were able to successfully implement an initial Machine Learning classification model for determining heterogeneity. However, the available dataset was insufficient to train a resilient and stable system. Therefore, we propose an architecture for a semi-automatic data collection and preprocessing framework easing the collection of large quantities of required data. While there exists a multitude of frameworks tackling parts of the tumor research area, no simple ready-to-use solution is present for the easy collection of tumor data in daily clinical routine, especially for high-resolution pathological images. We plan to implement the proposed architecture at the University Hospital Augsburg, Germany in 2023. The dataset created in this process will then be used as a resilient basis for heterogeneity classification and for analyzing glioblastomas in general.

The last three years showed that a deep understanding of pandemics, how they spread, and how they can be managed is of utmost importance. However, teaching students such complex topics is hard and cumbersome. Studies from other domains have already found, that students highly benefit from game-based learning approaches. Therefore, we developed a concept for a serious game, teaching students pandemic management. In this paper, we introduce our core gameplay concept, the methods of how requirements for such a serious game were derived as well as the final system architecture we developed to meet the identified requirements. We believe that such a game-based learning approach may significantly improve the understanding of pandemics and how to properly manage decisions in such a complex scenario.

Orofacial myofunctional disorders in children are a common problem with most therapy consisting of interventions to be done at home. Parents often find it difficult to motivate their children to exercise, and it is impossible for speech therapists to effectively track how much the children have practiced at home. Gamified mHealth applications have already been successfully applied in other domains of speech therapy, such as speech apraxia and aphasia. For orofacial myofunctional therapy, a sophisticated and well-established solution is still lacking. We describe the requirements and development of LudusMyo, an mHealth application to support OMD therapy specifically. We expect that such a gamified mHealth application will significantly increase the children's motivation to continue their therapy at home while enabling therapists to reliably track their patients' progress and tailor the outpatient therapy accordingly.