Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle
Simulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid–structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geom...
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MDPI AG
2024-12-01
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| author | Nikita Pil Alex G. Kuchumov |
| author_facet | Nikita Pil Alex G. Kuchumov |
| author_sort | Nikita Pil |
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| description | Simulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid–structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geometries to generate a training set. A method for the design of a synthetic database of geometric models is presented in this study. We suggest using synthetic geometries that enable the development of several aortic valve and left ventricular models in a range of sizes and shapes. In particular, we developed 22 variations of left ventricular geometries, including one original model, seven models with varying wall thicknesses, seven models with varying heights, and seven models with varying shapes. To guarantee anatomical accuracy and physiologically acceptable fluid volumes, these models were verified using actual patient data. Numerical simulations of left ventricle contraction and aortic valve leaflet opening/closing were performed to evaluate the electro-physiological potential distribution in the left ventricle and wall shear stress distribution in aortic valve leaflets. The proposed synthetic database aims to increase the predictive power of machine-learning models in cardiovascular research and, eventually, improve patient outcomes after aortic valve surgery. |
| format | Article |
| id | doaj-art-1f8ffa39e32a4c74abe62be8714e48bd |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1f8ffa39e32a4c74abe62be8714e48bd2025-01-10T13:20:32ZengMDPI AGSensors1424-82202024-12-012511110.3390/s25010011Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left VentricleNikita Pil0Alex G. Kuchumov1Biofluids Laboratory, Perm National Research Polytechnic University, 614990 Perm, RussiaBiofluids Laboratory, Perm National Research Polytechnic University, 614990 Perm, RussiaSimulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid–structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geometries to generate a training set. A method for the design of a synthetic database of geometric models is presented in this study. We suggest using synthetic geometries that enable the development of several aortic valve and left ventricular models in a range of sizes and shapes. In particular, we developed 22 variations of left ventricular geometries, including one original model, seven models with varying wall thicknesses, seven models with varying heights, and seven models with varying shapes. To guarantee anatomical accuracy and physiologically acceptable fluid volumes, these models were verified using actual patient data. Numerical simulations of left ventricle contraction and aortic valve leaflet opening/closing were performed to evaluate the electro-physiological potential distribution in the left ventricle and wall shear stress distribution in aortic valve leaflets. The proposed synthetic database aims to increase the predictive power of machine-learning models in cardiovascular research and, eventually, improve patient outcomes after aortic valve surgery.https://www.mdpi.com/1424-8220/25/1/11aortic valveleft ventricleparameterized geometrysynthetic data |
| spellingShingle | Nikita Pil Alex G. Kuchumov Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle Sensors aortic valve left ventricle parameterized geometry synthetic data |
| title | Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle |
| title_full | Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle |
| title_fullStr | Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle |
| title_full_unstemmed | Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle |
| title_short | Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle |
| title_sort | algorithmic generation of parameterized geometric models of the aortic valve and left ventricle |
| topic | aortic valve left ventricle parameterized geometry synthetic data |
| url | https://www.mdpi.com/1424-8220/25/1/11 |
| work_keys_str_mv | AT nikitapil algorithmicgenerationofparameterizedgeometricmodelsoftheaorticvalveandleftventricle AT alexgkuchumov algorithmicgenerationofparameterizedgeometricmodelsoftheaorticvalveandleftventricle |