Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes.
Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes.
复制标题
DOI：
10.3389/fcvm.2021.759675
复制
发表时间：
2021
影响因子：
3.6
通讯作者：
Kassab GS
中科院分区：
文献类型：
--
作者：
Dabiri Y;Yao J;Mahadevan VS;Gruber D;Arnaout R;Gentzsch W;Guccione JM;Kassab GS
重度二尖瓣返流（MR）是一种可导致致命后果的心脏病。二尖瓣夹系统（MC）介入是一种经皮手术，通过该手术使用MC沿着边缘连接二尖瓣（MV）瓣叶。MC干预的结果事先并不知道，即，结果是相当不稳定的。人工智能（AI）可用于指导心脏病专家选择最佳MC场景。在这项研究中，我们描述了一个地图集的形状，以及不同的情况下，MC植入这样的AI分析。我们从三个不同的来源生成MV几何数据。首先，使用患者的三维回波图像。从三个视图的回波图像中获得六个关键点的像素数据。使用PyGem（Python中的开源变形库），这些坐标用于通过变形模板几何体来创建几何体。其次，使用文献中的MV尺寸来创建数据。第三，我们使用机器学习方法，主成分分析和生成对抗网络来生成更多的形状。我们使用有限元（FE）软件ABAQUS来模拟MC干预的不同场景下的光滑粒子流体动力学。对瓣叶中的MR和应力进行后处理。我们基于物理的FE模型模拟了不同情况下MC干预的结果。通过FE模型计算了不同位置的单个成形夹以及两个和三个成形夹的瓣叶MR和应力。FE模拟的结果表明，MC的位置和数量会影响随后的残留MR，并且瓣叶应力不会遵循简单的模式。此外，FE模型需要几个小时才能提供结果，并且它们不适用于需要实时预测MC治疗结果的临床使用。在这项研究中，我们为AI模型生成了所需的数据集，这些数据集可以在几秒钟内提供结果。
Severe mitral regurgitation (MR) is a cardiac disease that can lead to fatal consequences. MitraClip (MC) intervention is a percutaneous procedure whereby the mitral valve (MV) leaflets are connected along the edge using MCs. The outcomes of the MC intervention are not known in advance, i.e., the outcomes are quite variable. Artificial intelligence (AI) can be used to guide the cardiologist in selecting optimal MC scenarios. In this study, we describe an atlas of shapes as well as different scenarios for MC implantation for such an AI analysis. We generated the MV geometrical data from three different sources. First, the patients' 3-dimensional echo images were used. The pixel data from six key points were obtained from three views of the echo images. Using PyGem, an open-source morphing library in Python, these coordinates were used to create the geometry by morphing a template geometry. Second, the dimensions of the MV, from the literature were used to create data. Third, we used machine learning methods, principal component analysis, and generative adversarial networks to generate more shapes. We used the finite element (FE) software ABAQUS to simulate smoothed particle hydrodynamics in different scenarios for MC intervention. The MR and stresses in the leaflets were post-processed. Our physics-based FE models simulated the outcomes of MC intervention for different scenarios. The MR and stresses in the leaflets were computed by the FE models for a single clip at different locations as well as two and three clips. Results from FE simulations showed that the location and number of MCs affect subsequent residual MR, and that leaflet stresses do not follow a simple pattern. Furthermore, FE models need several hours to provide the results, and they are not applicable for clinical usage where the predicted outcomes of MC therapy are needed in real-time. In this study, we generated the required dataset for the AI models which can provide the results in a matter of seconds.
登录
查看更多内容
影响因子：
3.5
作者：
Liang L;Liu M;Martin C;Elefteriades JA;Sun W
通讯作者：
Sun W
影响因子：
2.4
作者：
Dabiri, Yaghoub;Yao, Jiang;Guccione, Julius M.
通讯作者：
Guccione, Julius M.
影响因子：
4.6
作者：
Kamakoti, Ramji;Dabiri, Yaghoub;Kassab, Ghassan S.
通讯作者：
Kassab, Ghassan S.
影响因子：
3.1
作者：
Dabiri, Yaghoub;Van der Velden, Alex;Guccione, Julius M.
通讯作者：
Guccione, Julius M.
影响因子：
3.9
作者：
Liang, Liang;Liu, Minliang;Sun, Wei
通讯作者：
Sun, Wei