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A computational framework for personalising atrial fibrillation treatment to minimise heart failure risk

CORDIS · observation · Publication date unknown

linical value. In this fellowship, I propose to address the unmet need of personalising AF therapy to minimise the risk of HF (AFHF-TAILOR) by integrating whole-organ computer simulations powered by artificial intelligence (AI) and clinical data. The project takes in-silico studies to the next level, by integrating electro-mechanical simulations in thousands of virtual patients. Moreover, to ensure that the computer framework meets the needs for clinical translation, a dedicated AI framework will be trained on the simulations to enable rapid and accurate estimations of personalised cardiac mechanics. Two distinct clinical datasets, with important information such as cardiac imaging, catheter ablation lesions and mechanical biomarkers, will be used to externally validate the AI algorithm and the computer simulations. Accordingly, the project will provide a robust digital framework that balances ablation efficacy with the preservation of atrial function, ultimately reducing the risk of both diseases, AF and HF. Atrial fibrillation, Heart failure, Catheter ablation, Electro-mechanical

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recordType

award

status

SIGNED

region

EU

value

292118.88

unit

EUR