Abstract #4788
Identification of Hemodynamic Biomarkers for Bicuspid Aortic Valve induced Aortic Dilation using Machine Learning
Pamela Franco1,2,3, Julio Sotelo1,3,4, Andrea Guala5, Lydia Dux-Santoy5, Arturo Evangelista5, José Rodríguez-Palomares5, Domingo Mery6, Rodrigo Salas4, and Sergio Uribe1,3,7
1Biomedical Imaging Center, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 2Electrical Engineering Department, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 3Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, 4School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile, 5Department of Cardiology, Hospital Universitari Vall d’Hebron, Vall d’Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain, 6Deparment of Computer Science, Pontificia Universidad Católica de Chile, Santiago, Chile, 7Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
Synopsis
Several studies have demonstrated the existence of altered hemodynamics in bicuspid aortic valve (BAV) patients. The objective of this study was to identify which hemodynamic parameters allow an accurate classification between BAV patients with dilated and non-dilated ascending aorta using machine learning (ML) algorithms.
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