ARTEMIS aims at making a major leap forward in the management of metabolic associated fatty liver disease (MAFLD) - the leading cause of chronic liver disease in Europe, surpassing both viral hepatitis and alcohol-related causes.
The project hopes to control this fast-growing metabolic syndrome and its complications by leveraging advanced liver and heart Virtual Twins.
ARTEMIS will co-design, develop, and evaluate a clinical decision support system (CDSS) to help for decision-making towards improved patient management strategies for MAFLD patients, leading to the development of personalised diagnostics and innovative therapeutic strategies.
Specifically, ARTEMIS has the ambition to enable early disease detection, prediction of disease progression, and effective disease management. This will ultimately contribute to promoting real-world data and AI for secure and ethical decision-making in healthcare.
The four year project, which started in January 2024, has been funded by a €10million grant from the European Union under the Horizon Europe scheme and brings together 22 institutions - including hospitals, academia, regulatory experts, high-tech SMEs and a patient association - from nine countries, who are joining forces to revolutionalise the management of MAFLD.
Assessing the capacity of digital twins for better management of MAFLD
From a technical perspective, ARTEMIS aims to consolidate and couple existing multilevel computational models for the virtual liver - representative of signal transduction, metabolism, tissue mechanics, blood flow and transport - to extend them to a multi-organ approach (extended to the heart, the systemic and splanchnic circulatory system), through both mechanistic and machine-learning approaches.
The models will be validated and evaluated in clinical practice through four proof-of-concept demonstrators (Liver disease staging in MASLD patients; MASLD and cardiovascular diseases; MASLD and transjugular intrahepatic portosystemic shunt (TIPS); MASLD and Transplants) to assess the capacity of digital twins to provide clinically meaningful information to healthcare professionals, for the better management of MAFLD patients.
Various groups will participate, including: Molecular Medical Imaging, Liver Diseases, Diabetes and Metabolism, Cardiovascular Disease, Clinical Biochemistry, Drug Delivery and Therapy.
Bournemouth University, under the lead of Professor Hamid Bouchachia, will participate in the development of AI-based digital tools for the digitisation and personalisation of the virtual twins.
In particular, BU will lead the work package on data exploration, clustering and pattern identification, as well as various tasks related to this work package using longitudinal and multi-modal data (clinical data, multi-omics, biology samples, imaging data), design of the decision support system, federated platform, and design and development of predictive machine learning algorithms.