Project Full Title: AcceleRating the Translation of virtual twins towards a pErsonalised Management of steatotic liver patients
Project acronym: ARTEMIs
Project type: Horizon Europe | RIA (Topic HORIZON-HLTH-2023-TOOL-05-03)
Grant agreement no: 101136299
The Communication and Dissemination plan will give a summary on the different communication and dissemination activities that will be organised to maximise the impact of the project and make sure the findings and the developed tools reach a diverse audience. The plan also identifies the main target groups, the most effective channels for each one of them, as well as the different strategies that will be in place to keep them engagement throughout the entirety of the ARTEMIs project to ensure maximum relevancy for the patients, the general public and healthcare providers. The main goal of the ARTEMIs project is to co-design, develop, and evaluate a clinical decision support system (CDSS) for managing MASLD (Metabolic dysfunction-Associated Steatotic Liver Disease) patients. This involves collaboration between technologists, clinicians, and patients. The CDSS will:
-Provide an instant overview of a patient’s multimodal data.
-Use integrated virtual twin models to predict disease progression, cardiovascular outcomes, and responses to treatments or lifestyle changes.
-Enable personalized management of MASLD through dynamic representations of tissues or organs.
-Serve as an educational tool for both physicians and patients to promote better nutrition, lifestyle habits, and treatment adherence.
The development of the CDSS is driven by the needs of clinical practitioners and involves a multidisciplinary team, including model developers, clinical researchers, and social science and humanities (SSH) experts. This team will ensure that the CDSS meets clinical needs, is compatible with healthcare IT systems, and uses both existing and newly collected data. By the end of the decade, it is expected that this integrated solution will be in routine clinical practice, supported by funding from the Next Generation Europe and Digital Europe Programme. A federated data exploration platform will be used to access multicentric, multimodal, longitudinal data. This platform will help adapt machine-learning and mechanistic models to address critical clinical questions about MASLD progression and its cardiovascular risks. Multidisciplinary clinical panels will assess the utility and relevance of the CDSS prototype.
