TRUSTroke proposes a novel trustworthy by design and privacy-preserving AI-based platform to assist clinicians, patients and caregivers in the management of acute and chronic phases of ischemic stroke, based on the integration of clinical and patient-reported data, outcomes and experience for a trustworthy assessment of disease progression and risks. enabling more personalized and effective management of stroke, as well as providing inter-hospital benchmarking and sharing best practices.
To this purpose, a Federated Learning infrastructure will enable multiple clinical sites to build several trustworthy AI based predictive models by leveraging stroke data without compromising privacy and implementing best-in-class security and privacy protocols.
FAIRified clinical data from leading European hospitals and outpatient monitored data from a remote home-care system, will be used to train and validate trustworthy AI models for stroke prediction, to personalise patients´ assessment of cardiovascular risk factors, treatment compliance and communication with healthcare professionals.
Partners: Vall d’Hebron Institute of Research (VHIR) (coordinator), KU Leuven, FPG, UL), the European Organization for Nuclear Research (CERN), Eurecat (EUT), Politecnico di Milano (POLIMI), Consiglio Nazionale delle Ricerche-IEIIT, JSI, EATRIS and two SMEs.
IEIIT is responsible of Federated learning (FL) algorithms and related optimization in FL infrastructure in WP2, led by CERN, and is devoted to the design and development of the FL infrastructure, the implementation and validation of the federated system composed of different hospitals across Europe