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Prognostic assessment of valvular aortic disease treatment coupling Immunological and biomechanical profiles

CORDIS · observation · Publication date unknown

nd assess whether the proposed treatment is likely to be beneficial preoperatively, while also minimizing the risk of post-procedural complications. I will achieve this by combining imaging analysis, deep learning algorithms, in silico models, and in vitro tests. My approach involves the following key objectives: (i) creating a multi-physics digital twin of patients with aortic valve diseases, (ii) developing a validated, high-fidelity model for treatment with quantification of post-treatment outcomes and (iii) generating a proof of concept for a clinically applicable predictive model trained using both immunological profiles and biomechanical features of patients. This innovative approach will provide a deeper understanding of how clinical and biomechanical outcomes correlate with the amplification of inflammation, helping us comprehend the interaction between biomarkers and negative post-treatment prognosis in patients with aortic valve diseases. Personalised medicine, digital human twin in healthcare, treatment, validation, verification, treatment selection support, aortic valve d

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recordType

award

status

SIGNED

region

EU

value

1498295

unit

EUR