Computational modeling of bicuspid aortopathy: Towards personalized risk strategies

Federica Cosentino, Leonardo D'Acquisto, Valentina Agnese, Salvatore Pasta, Francesco Scardulla, Michele Pilato, Diego Bellavia, Giuseppe Raffa, Giovanni Gentile, Salvatore Pasta, Valentina Agnese, Federica Cosentino, Giovanni Domenico Gentile

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This paper describes current advances on the application of in-silico for the understanding of bicuspid aortopathy and future perspectives of this technology on routine clinical care. This includes the impact that artificial intelligence can provide to develop computer-based clinical decision support system and that wearable sensors can offer to remotely monitor high-risk bicuspid aortic valve (BAV) patients. First, we discussed the benefit of computational modeling by providing tangible examples of in-silico software products based on computational fluid-dynamic (CFD) and finite-element method (FEM) that are currently transforming the way we diagnose and treat cardiovascular diseases. Then, we presented recent findings on computational hemodynamic and structural mechanics of BAV to highlight the potentiality of patient-specific metrics (not-based on aortic size) to support the clinical-decision making process of BAV-associated aneurysms. Examples of BAV-related personalized healthcare solutions are illustrated.
Original languageEnglish
Pages (from-to)122-131
Number of pages10
JournalJournal of Molecular and Cellular Cardiology
Volume131
Publication statusPublished - 2019

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Cardiology and Cardiovascular Medicine

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