The disease resulting in the formation, growth, and rupture of intracranial aneurysms is complex. Research is accumulating evidence that the disease is driven by many different factors, some constant and others variable over time. Combinations of factors may induce specific biophysical reactions at different stages of the disease. A better understanding of the biophysical mechanisms responsible for the disease initiation and progression is essential to predict the natural history of the disease. More accurate predictions are mandatory to adequately balance risks between observation and intervention at the individual level as expected in the age of personalized medicine. Multidisciplinary exploration of the disease also opens an avenue to the discovery of possible preventive actions or medical treatments. Modern information technologies and data processing methods offer tools to address such complex challenges requiring 1) the collection of a high volume of information provided globally, 2) integration and harmonization of the information, and 3) management of data sharing with a broad spectrum of stakeholders.
Over the last decade an infrastructure has been set up and is now made available to the academic community to support and promote exploration of intracranial disease, modeling, and clinical management simulation and monitoring.
The background and purpose of the infrastructure is reviewed. The infrastructure data flow architecture is presented. The basic concepts of disease modeling that oriented the design of the core information model are explained. Disease phases, milestones, cases stratification group in each phase, key relevant factors, and outcomes are defined. Data processing and disease model visualization tools are presented. Most relevant contributions to the literature resulting from the exploitation of the infrastructure are reviewed, and future perspectives are discussed.