用深度学习从术前影像预测主动脉瓣植入术后反流风险
Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation
- 基于术前心脏CT的3D卷积网络分析解剖特征
- 模型可识别细微结构，提前预测术后反流发生
- 适合心血管介入医生用于术前风险评估
严重主动脉狭窄是老年人中常见且危及生命的疾病，常通过经导管主动脉瓣植入术（TAVI）治疗。尽管手术技术不断进步，术后瓣周主动脉反流（PVR）仍是常见并发症，且对远期预后有明确影响。本文研究了深度学习从术前心脏CT预测PVR发生的潜力。收集了术前TAVI患者的数据集，并在各向同性CT体积上训练3D卷积神经网络。结果表明，体素级深度学习能够从术前影像中捕捉到细微的解剖特征，为个性化风险评估和手术优化开辟新路径。源代码可在https://github.com/EIDOSLAB/tavi获取。
原文摘要 · Abstract (English)
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.
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