Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis
The study highlights the potential of integrating computational predictions, advanced machine learning techniques, and synthetic data generation to improve predictive accuracy and assess TAVI-related outcomes through in silico trials.
Authors
- Caterina Maria Gandolfo (ORCID: https://orcid.org/0000-0001-6049-8961)
- Diego Bellavia (ORCID: https://orcid.org/0000-0003-3808-2868)
- Salvatore Castelbuono (ORCID: https://orcid.org/0009-0002-8262-8223)
- Stefano Cannata (ORCID: https://orcid.org/0000-0002-0559-7833)
- Salvatore Pasta (ORCID: https://orcid.org/0000-0002-4841-2560)
- Giovanni Gentile (ORCID: https://orcid.org/0000-0003-3767-815X)
- Roberta Scuoppo
- Valentina Agnese (ORCID: https://orcid.org/0000-0002-0548-0926)
Institutions
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione (IT)
- University of Palermo (IT)
Publication Details
- Journal
- Medical & Biological Engineering & Computing
- Published
- 2024-10-10
- DOI
- https://doi.org/10.1007/s11517-024-03215-8
- Citations
- 6
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 2.41
Funders
- Università degli Studi di Palermo
- Horizon 2020 Framework Programme