An Open-Source Monitoring Framework for Data Exploration and Progress Tracking in Multi-Center Radiology Studies
2026-06-15 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created a simple tool that helps researchers keep track of progress in medical studies involving many different hospitals. Instead of using slow and outdated methods like emails and spreadsheets, their tool collects data from all sites and shows it clearly on dashboards. They tested this tool in a large study network across Germany, proving it can safely share information without revealing private details. This helps scientists coordinate better and manage big studies more efficiently.
multi-center studiesmedical imagingstudy monitoringdata visualizationGrafanaPrometheusprivacy-preservingKaapana platformdistributed researchresearch coordination
Authors
Markus Bujotzek, Jonas Scherer, Stefan Denner, Peter Neher, Benjamin Hamm, Lorenz Feineis, Uenal Akuenal, Andreas Bucher, Tobias Penzkofer, Klaus Maier-Hein
Abstract
Multi-center studies are crucial for advancing medical and radiological research. Data exploration, collaboration discovery, and study progress monitoring are essential for maximizing their potential. However, in practice these processes often rely on manual communication and shared tables, which quickly become outdated and hinder efficient coordination in large distributed studies. This highlights the need for dedicated monitoring solutions that provide transparent and up-to-date insights into study progress. We propose a lightweight, open-source monitoring architecture for multi-center studies based on the widely used Grafana-Prometheus stack. The framework collects aggregated monitoring metrics from distributed study sites and visualizes them through configurable dashboards. As a real-world deployment example, the framework is integrated into the medical imaging platform Kaapana and evaluated within a large multi-center research network. By deploying our solution within the Germany-wide RACOON consortium, we demonstrate its ability to enable privacy-preserving data exploration and study progress monitoring across all 38 German university clinics. The monitoring framework supports transparent coordination of distributed research activities and can facilitate more efficient management of large-scale multi-center studies. The source code and Kaapana integration are publicly available at https://github.com/MIC-DKFZ/study-monitoring-kaapana.