The Surviral Epidemiological Early Warning System Tyrol Dashboard - System, Functionality and Findings.

BACKGROUND: Dashboards provide a good retrospective view of the development of the disease. Yet, current COVID-related dashboards typically lack the capability to predict future trends. However, this is important for health policy makers and health care providers in order to adopt meaningful containment strategies. OBJECTIVES: The aim of this paper is to present the Surviral dashboard, which allows the effective monitoring of infectious disease dynamics. METHODS: The presented dashboard comprises a wide range of information, including retrospective and prognostic data based on an agent-based simulation framework. It served as the basis for informed decision-making and planning of disease control strategies within the federal state of Tyrol. RESULTS: By visualizing the information in an understandable format, the dashboard provided a comprehensive overview of the COVID-19 situation in Tyrol and allowed for the identification of trends and patterns. CONCLUSION: The presented dashboard is a valuable tool for managing pandemics such as COVID-19. It provides a convenient and efficient way to monitor the spread of a disease and identify potential areas for intervention.

Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach.

PURPOSE: In radiation therapy, a key step for a successful cancer treatment is image-based treatment planning. One objective of the planning phase is the fast and accurate segmentation of organs at risk and target structures from medical images. However, manual delineation of organs, which is still the gold standard in many clinical environments, is time-consuming and prone to inter-observer variations. Consequently, many automated segmentation methods have been developed. METHODS: In this work, we train two hierarchical 3D neural networks to segment multiple organs at risk in the head and neck area. First, we train a coarse network on size-reduced medical images to locate the organs of interest. Second, a subsequent fine network on full-resolution images is trained for a final accurate segmentation. The proposed method is purely deep learning based; accordingly, no pre-registration or post-processing is required. RESULTS: The approach has been applied on a publicly available computed tomography dataset, created for the MICCAI 2015 Auto-Segmentation challenge. In an extensive evaluation process, the best configurations for the trained networks have been determined. Compared to the existing methods, the presented approach shows state-of-the-art performance for the segmentation of seven different structures in the head and neck area. CONCLUSION: We conclude that 3D neural networks outperform the most existing model- and atlas-based methods for the segmentation of organs at risk in the head and neck area. The ease of use, high accuracy and the test time efficiency of the method make it promising for image-based treatment planning in clinical practice.