Work

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

Year: 2023

Type: article

Study design: Observational Study

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2023.107052

Authors: Leonardo Geronzi, Antonio Martinez, Michel Rochette, Kexin Yan, Aline Bel-Brunon, Pascal Haigron, Pierre Escrig, Jacques Tomasi, Morgan Daniel, Alain Lalande, Siyu Lin, Diana Marcela Marin-Castrillon, Olivier Bouchot, Jean Porterie, Pier Paolo Valentini, Marco Evangelos Biancolini

Institutions: University of Rome Tor Vergata, Ansys (France), Centre National de la Recherche Scientifique, Université de Lyon, Laboratoire de Mécanique des Contacts et des Structures, Institut National des Sciences Appliquées de Lyon, Inserm, Laboratoire Traitement du Signal et de l'Image, Hôpital Pontchaillou, Centre Hospitalier Universitaire de Rennes, Université de Rennes, Université de Bourgogne, CHU Dijon Bourgogne, Institut de Chimie Moléculaire de l'Université de Bourgogne, Centre Hospitalier Universitaire de Toulouse, Hôpital Rangueil, Hôpital Purpan, Hôpital Paule de Viguier, Hôpital de La Grave

Language: English

Topic: Aortic Disease and Treatment Approaches

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: European Commission, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, Horizon 2020, H2020 Marie Skłodowska-Curie Actions

Awards: The Medical Digital Twin for Aneurysm Prevention and Treatment, G6201326806

OpenAlex ID: W4378190884

FWCI: 4.23

Cites: 90

Cited by: 24

Related to: 10