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.

翻译：重度主动脉瓣狭窄是老年患者常见且危及生命的疾病，通常采用经导管主动脉瓣植入术（TAVI）进行治疗。尽管手术技术不断进步，瓣周主动脉瓣反流（PVR）仍是TAVI术后最常见的并发症之一，已被证实对长期预后具有显著影响。本研究探讨了利用深度学习从术前心脏CT预测PVR发生的潜力。为此，我们收集了TAVI术前患者的数据集，并在各向同性CT容积数据上训练了三维卷积神经网络。所获结果表明，容积深度学习能够从TAVI术前影像中捕捉细微的解剖学特征，为个性化风险评估和手术方案优化开辟了新前景。源代码发布于 https://github.com/EIDOSLAB/tavi。