Promozione della Salute, Materno-Infantile, di Medicina Interna e Specialistica di Eccellenza "G. D'Alessandro", University of Palermo, Piazza delle Cliniche, n.2, 90128 Palermo, Italy; Fondazione Ri.MED, Via Bandiera n.11, 90133 Palermo, Italy.
Department of Engineering, University of Palermo, Viale delle Scienze Ed.8, 90128 Palermo, Italy.
J Mol Cell Cardiol. 2019 Jun;131:122-131. doi: 10.1016/j.yjmcc.2019.04.026. Epub 2019 May 4.
This paper describes current advances on the application of in-silico for the understanding of bicuspid aortopathy and future perspectives of this technology on routine clinical care. This includes the impact that artificial intelligence can provide to develop computer-based clinical decision support system and that wearable sensors can offer to remotely monitor high-risk bicuspid aortic valve (BAV) patients. First, we discussed the benefit of computational modeling by providing tangible examples of in-silico software products based on computational fluid-dynamic (CFD) and finite-element method (FEM) that are currently transforming the way we diagnose and treat cardiovascular diseases. Then, we presented recent findings on computational hemodynamic and structural mechanics of BAV to highlight the potentiality of patient-specific metrics (not-based on aortic size) to support the clinical-decision making process of BAV-associated aneurysms. Examples of BAV-related personalized healthcare solutions are illustrated.
本文介绍了计算机模拟在理解二叶式主动脉瓣病变中的应用现状和该技术在常规临床护理中的未来展望。其中包括人工智能在开发基于计算机的临床决策支持系统方面的作用，以及可穿戴传感器在远程监测高危二叶式主动脉瓣（BAV）患者方面的作用。首先，我们通过提供基于计算流体动力学（CFD）和有限元方法（FEM）的计算机模拟软件产品的实际案例，讨论了计算建模的好处，这些产品目前正在改变我们诊断和治疗心血管疾病的方式。然后，我们介绍了 BAV 的计算血流动力学和结构力学的最新发现，以突出基于患者个体特征（而非主动脉大小）的指标在支持 BAV 相关动脉瘤临床决策中的潜力。还举例说明了与 BAV 相关的个性化医疗解决方案。
