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Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes
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 ou...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709129/ https://www.ncbi.nlm.nih.gov/pubmed/34957251 http://dx.doi.org/10.3389/fcvm.2021.759675 |
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author | Dabiri, Yaghoub Yao, Jiang Mahadevan, Vaikom S. Gruber, Daniel Arnaout, Rima Gentzsch, Wolfgang Guccione, Julius M. Kassab, Ghassan S. |
author_facet | Dabiri, Yaghoub Yao, Jiang Mahadevan, Vaikom S. Gruber, Daniel Arnaout, Rima Gentzsch, Wolfgang Guccione, Julius M. Kassab, Ghassan S. |
author_sort | Dabiri, Yaghoub |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8709129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87091292021-12-25 Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes Dabiri, Yaghoub Yao, Jiang Mahadevan, Vaikom S. Gruber, Daniel Arnaout, Rima Gentzsch, Wolfgang Guccione, Julius M. Kassab, Ghassan S. Front Cardiovasc Med Cardiovascular Medicine 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. Frontiers Media S.A. 2021-12-10 /pmc/articles/PMC8709129/ /pubmed/34957251 http://dx.doi.org/10.3389/fcvm.2021.759675 Text en Copyright © 2021 Dabiri, Yao, Mahadevan, Gruber, Arnaout, Gentzsch, Guccione and Kassab. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine Dabiri, Yaghoub Yao, Jiang Mahadevan, Vaikom S. Gruber, Daniel Arnaout, Rima Gentzsch, Wolfgang Guccione, Julius M. Kassab, Ghassan S. Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes |
title | Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes |
title_full | Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes |
title_fullStr | Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes |
title_full_unstemmed | Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes |
title_short | Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes |
title_sort | mitral valve atlas for artificial intelligence predictions of mitraclip intervention outcomes |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709129/ https://www.ncbi.nlm.nih.gov/pubmed/34957251 http://dx.doi.org/10.3389/fcvm.2021.759675 |
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