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CARDIOTRIALS

Financiado

Using CARDIac simulations to run in-silicO clinical TRIALS

Clinical trials are a key tool for advancing medical knowledge, but they consist of a long and costly process entailing the recruitment of a representative cohort of participants to properly account for the population statistical... ver más

30/09/2027

GSSI

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

GRAN SASSO SCIENCE INSTITUTE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-04-22

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2021-STG: ERC STARTING GRANTS

I+D

Cerrada hace 3 años

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1 Participantes

GSSI

1.50M€ | Lider

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Últimas noticias

01-12-2024: REDES En las últimas 48 horas el Organismo REDES ha otorgado 1337 concesiones

01-12-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

01-12-2024: AEI En las últimas 48 horas el Organismo AEI ha otorgado 23 concesiones

Duración del proyecto: 65 meses Fecha Inicio: 2022-04-22
Fecha Fin: 2027-09-30

Líder del proyecto

GRAN SASSO SCIENCE INSTITUTE

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto Clinical trials are a key tool for advancing medical knowledge, but they consist of a long and costly process entailing the recruitment of a representative cohort of participants to properly account for the population statistical variability. Computational engineering is a promising approach to gain more insight into patients' cardiac pathologies and to test innovative medical devices before running conclusive in-vivo experiments on animals or medical trials on humans. This technological breakthrough, however, is limited by some technical and epistemic challenges: (i) the reliability of cardiovascular computational models depends on the accurate solution of the hemodynamics coupled with the deforming biologic tissues; (ii) the resulting multi-physics solver requires an immense computational power and long time-to-results; (iii) a great variability among individuals exists, thus calling for a statistical approach. For the first time I will accomplish and employ a computational platform for determining the outcome of pathologies or devices implantation by combining my GPU-accelerated multi-physics solver for the accurate solution of cardiac dynamics with an uncertainty quantification analysis to account for the individuals variability. The input parameters of the computational model will be treated as aleatory variables, whose probability distribution function will be obtained using three-dimensional datasets of cardiac configurations available to the PI's group and acquired in-vivo by the clinical members involved in the project. Simulation campaigns (rather than a single simulation) will be then run in order to sweep the uncertain input distributions and obtain the synthetic population response in the case of selected pathologies like myocardial infarction and the optimal stimulation pattern for cardiac resynchronization therapy. My approach removes the main barrier that keeps up from a systematic use of computational engineering to run in-silico clinical trials.

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