Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

Yaghoub Dabiri; Jiang Yao; Vaikom S Mahadevan; Daniel Gruber; Rima Arnaout; Wolfgang Gentzsch; Julius M Guccione; Ghassan S Kassab

doi:10.3389/fcvm.2021.759675

Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

Front Cardiovasc Med. 2021 Dec 10:8:759675. doi: 10.3389/fcvm.2021.759675. eCollection 2021.

Authors

Yaghoub Dabiri¹, Jiang Yao², Vaikom S Mahadevan³, Daniel Gruber⁴, Rima Arnaout^{5

6

7

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9}, Wolfgang Gentzsch⁴, Julius M Guccione¹⁰, Ghassan S Kassab¹¹

Affiliations

¹ 3DT Holdings LLC, San Diego, CA, United States.
² Dassault Systemes Simulia Corp, Johnston, RI, United States.
³ Department of Medicine, University of California, San Francisco, San Francisco, CA, United States.
⁴ The UberCloud, Sunnyvale, CA, United States.
⁵ Division of Cardiology, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States.
⁶ Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.
⁷ Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, United States.
⁸ Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, United States.
⁹ Chan Zuckerberg Biohub, University of California, San Francisco, San Francisco, CA, United States.
¹⁰ Department of Surgery, University of California, San Francisco, San Francisco, CA, United States.
¹¹ Department of Medicine, California Medical Innovations Institute, San Diego, CA, United States.

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.

Keywords: MitraClip; cardiac imaging; finite element method; mitral valve; principal component analysis.

Grants and funding

R01 HL150394/HL/NHLBI NIH HHS/United States