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Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation

Fuente: arXiv

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Auteurs principaux:	Cannito, Michele, Renzulli, Riccardo, Duarte, Adson, Nikfam, Farzad, Barbano, Carlo Alberto, Chiesa, Enrico, Bruno, Francesco, Giacobbe, Federico, Wanha, Wojciech, Giordano, Arturo, Grangetto, Marco, D'Ascenzo, Fabrizio
Format:	Preprint
Publié:	2026
Sujets:	Computer Vision and Pattern Recognition Artificial Intelligence
Accès en ligne:	Acceder al recurso
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author	Cannito, Michele Renzulli, Riccardo Duarte, Adson Nikfam, Farzad Barbano, Carlo Alberto Chiesa, Enrico Bruno, Francesco Giacobbe, Federico Wanha, Wojciech Giordano, Arturo Grangetto, Marco D'Ascenzo, Fabrizio
author_facet	Cannito, Michele Renzulli, Riccardo Duarte, Adson Nikfam, Farzad Barbano, Carlo Alberto Chiesa, Enrico Bruno, Francesco Giacobbe, Federico Wanha, Wojciech Giordano, Arturo Grangetto, Marco D'Ascenzo, Fabrizio
contents	Severe aortic stenosis is a common and life-threatening condition in elderly patients, often treated with Transcatheter Aortic Valve Implantation (TAVI). Despite procedural advances, paravalvular aortic regurgitation (PVR) remains one of the most frequent post-TAVI complications, with a proven impact on long-term prognosis. In this work, we investigate the potential of deep learning to predict the occurrence of PVR from preoperative cardiac CT. To this end, a dataset of preoperative TAVI patients was collected, and 3D convolutional neural networks were trained on isotropic CT volumes. The results achieved suggest that volumetric deep learning can capture subtle anatomical features from pre-TAVI imaging, opening new perspectives for personalized risk assessment and procedural optimization. Source code is available at https://github.com/EIDOSLAB/tavi.
format	Preprint
id	arxiv_https___arxiv_org_abs_2602_13842
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation Cannito, Michele Renzulli, Riccardo Duarte, Adson Nikfam, Farzad Barbano, Carlo Alberto Chiesa, Enrico Bruno, Francesco Giacobbe, Federico Wanha, Wojciech Giordano, Arturo Grangetto, Marco D'Ascenzo, Fabrizio Computer Vision and Pattern Recognition Artificial Intelligence Severe aortic stenosis is a common and life-threatening condition in elderly patients, often treated with Transcatheter Aortic Valve Implantation (TAVI). Despite procedural advances, paravalvular aortic regurgitation (PVR) remains one of the most frequent post-TAVI complications, with a proven impact on long-term prognosis. In this work, we investigate the potential of deep learning to predict the occurrence of PVR from preoperative cardiac CT. To this end, a dataset of preoperative TAVI patients was collected, and 3D convolutional neural networks were trained on isotropic CT volumes. The results achieved suggest that volumetric deep learning can capture subtle anatomical features from pre-TAVI imaging, opening new perspectives for personalized risk assessment and procedural optimization. Source code is available at https://github.com/EIDOSLAB/tavi.
title	Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation
topic	Computer Vision and Pattern Recognition Artificial Intelligence
url	https://arxiv.org/abs/2602.13842