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[2602.13842] Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation
Summary
This paper explores the use of deep learning to predict paravalvular regurgitation (PVR) in patients undergoing Transcatheter Aortic Valve Implantation (TAVI), highlighting the potential of 3D convolutional neural networks to enhance preoperative risk assessment.
Why It Matters
Predicting PVR before TAVI is crucial as it can significantly affect patient outcomes. This research leverages advanced deep learning techniques to improve the accuracy of preoperative assessments, potentially leading to better surgical planning and patient care.
Key Takeaways
- Deep learning can effectively predict PVR from preoperative cardiac CT images.
- 3D convolutional neural networks capture subtle anatomical features that traditional methods may miss.
- Improved risk assessment can lead to personalized treatment plans for TAVI patients.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13842 (cs) [Submitted on 14 Feb 2026] Title:Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation Authors:Michele Cannito, Riccardo Renzulli, Adson Duarte, Farzad Nikfam, Carlo Alberto Barbano, Enrico Chiesa, Francesco Bruno, Federico Giacobbe, Wojciech Wanha, Arturo Giordano, Marco Grangetto, Fabrizio D'Ascenzo View a PDF of the paper titled Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation, by Michele Cannito and Riccardo Renzulli and Adson Duarte and Farzad Nikfam and Carlo Alberto Barbano and Enrico Chiesa and Francesco Bruno and Federico Giacobbe and Wojciech Wanha and Arturo Giordano and Marco Grangetto and Fabrizio D'Ascenzo View PDF HTML (experimental) Abstract: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 ca...
