Tahir Anas M, Mutlu Onur, Bensaali Faycal, Ward Rabab, Ghareeb Abdel Naser, Helmy Sherif M H A, Othman Khaled T, Al-Hashemi Mohammed A, Abujalala Salem, Chowdhury Muhammad E H, Alnabti A Rahman D M H, Yalcin Huseyin C
Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Biomedical Research Center, Qatar University, Doha 2713, Qatar.
J Clin Med. 2023 Jul 19;12(14):4774. doi: 10.3390/jcm12144774.
Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.
主动脉瓣缺陷是最常见的临床病症之一。严重受损或无法正常工作的主动脉瓣通常通过经导管主动脉瓣置换术（TAVR）用生物人工心脏瓣膜（BHV）进行置换。准确的术前规划对于TAVR手术的成功结果至关重要。计算流体动力学（CFD）、有限元分析（FEA）和流固耦合（FSI）分析的评估提供了一种越来越多地用于评估BHV力学和动力学的解决方案。然而，高计算成本和计算建模的复杂操作阻碍了其应用。深度学习（DL）领域的最新进展可以提供一种实时替代方法，能够在几秒钟内呈现血流动力学参数，从而指导临床医生选择最佳治疗方案。在此，我们对用于TAVR规划和结果评估的经典计算建模方法、医学成像和DL方法进行全面综述。特别是，我们关注先前研究中的DL方法，突出所使用的数据集、部署的DL模型以及取得的结果。我们强调关键挑战，并为创新研究人员推荐几个未来的研究方向。最后，概述了一个用于实时评估和推荐TAVR最佳BHV设计的端到端智能DL框架。最终，在未来研究中部署这样一个框架将支持临床医生在TAVR治疗规划期间将风险降至最低，并有助于改善患者护理。
