Abstract
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.
Highlights
MitraClip (MR) is a percutaneous procedure to treat severe mitral regurgitation (MR), a disease with a prevalence of 4 million in US and an incidence of 250,000 patients each year [1, 2]
In the initial epochs the geometries provided by generative adversarial networks (GANs) were randomly distributed, the generated geometries became closer to the mitral valve (MV) (Figure 5)
Our approach is based on different data-generation methods, namely patients’ data, literature reports, principal component analysis (PCA), and GANs
Summary
MitraClip (MR) is a percutaneous procedure to treat severe mitral regurgitation (MR), a disease with a prevalence of 4 million in US and an incidence of 250,000 patients each year [1, 2]. This intervention is most appropriate for patients who cannot tolerate surgery because of other diseases or poor health conditions [2]. The location of the MC along the leaflet edges and the number of MCs has a direct role in the effectiveness of MC therapy. Leaflet injury can occur as a result of MC intervention [6, 7], which is another consideration for an optimal MC therapy
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