Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization

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• dc.contributor.author Danilov, Viacheslav V.
• dc.contributor.author Klyshnikov, Kirill Yu
• dc.contributor.author Onishenko, Pavel S.
• dc.contributor.author Proutski, Alex
• dc.contributor.author Gankin, Yuriy
• dc.contributor.author Melgani, Farid
• dc.contributor.author Ovcharenko, Evgeny A.
• dc.date.accessioned 2025-11-04T06:43:48Z
• dc.date.available 2025-11-04T06:43:48Z
• dc.date.issued 2023
• dc.description.abstract Majority of modern techniques for creating and optimizing the geometry of medical devices are based on a combination of computer-aided designs and the utility of the finite element method This approach, however, is limited by the number of geometries that can be investigated and by the time required for design optimization. To address this issue, we propose a generative design approach that combines machine learning (ML) methods and optimization algorithms. We evaluate eight different machine learning methods, including decision tree-based and boosting algorithms, neural networks, and ensembles. For optimal design, we investigate six state-of-the-art optimization algorithms, including Random Search, Tree-structured Parzen Estimator, CMA-ES-based algorithm, Nondominated Sorting Genetic Algorithm, Multiobjective Tree-structured Parzen Estimator, and Quasi-Monte Carlo Algorithm. In our study, we apply the proposed approach to study the generative design of a prosthetic heart valve (PHV). The design constraints of the prosthetic heart valve, including spatial requirements, materials, and manufacturing methods, are used as inputs, and the proposed approach produces a final design and a corresponding score to determine if the design is effective. Extensive testing leads to the conclusion that utilizing a combination of ensemble methods in conjunction with a Tree-structured Parzen Estimator or a Nondominated Sorting Genetic Algorithm is the most effective method in generating new designs with a relatively low error rate. Specifically, the Mean Absolute Percentage Error was found to be 11.8% and 10.2% for lumen and peak stress prediction respectively. Furthermore, it was observed that both optimization techniques result in design scores of approximately 95%. From both a scientific and applied perspective, this approach aims to select the most efficient geometry with given input parameters, which can then be prototyped and used for subsequent in vitro experiments. By proposing this approach, we believe it will replace or complement CAD-FEM-based modeling, thereby accelerating the design process and finding better designs within given constraints. The repository, which contains the essential components of the study, including curated source code, dataset, and trained models, is publicly available at https://github.com/ViacheslavDanilov/generative_design.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Danilov VV, Klyshnikov KY, Onishenko PS, Proutski A, Gankin Y, Melgani F, et al. Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization. Front Bioeng Biotechnol. 2023 Sep 15;11:1238130. DOI: 10.3389/fbioe.2023.1238130
• dc.identifier.doi http://dx.doi.org/10.3389/fbioe.2023.1238130
• dc.identifier.issn 2296-4185
• dc.identifier.uri http://hdl.handle.net/10230/71742
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in Bioengineering and Biotechnology. 2023 Sep 15;11:1238130
• dc.rights © 2023 Danilov, Klyshnikov, Onishenko, Proutski, Gankin, Melgani and Ovcharenko. 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.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Generative designen
• dc.subject.keyword Heart valve prosthesisen
• dc.subject.keyword Prosthetic heart valveen
• dc.subject.keyword Machine learningen
• dc.subject.keyword Optimizationen
• dc.subject.keyword Gradient methodsen
• dc.subject.keyword Computer-aided designen
• dc.subject.keyword Finite element methoden
• dc.title Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimizationen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion