Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms
Abstract Developing new capabilities to predict the risk of intracranial aneurysm rupture and to improve treatment outcomes in the follow-up of endovascular repair is of tremendous medical and societal interest, both to support decision-making and assessment of treatment options by medical doctors,...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34007-z |
| _version_ | 1797832107027857408 |
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| author | E. Hachem P. Meliga A. Goetz P. Jeken Rico J. Viquerat A. Larcher R. Valette A. F. Sanches V. Lannelongue H. Ghraieb R. Nemer Y. Ozpeynirci T. Liebig |
| author_facet | E. Hachem P. Meliga A. Goetz P. Jeken Rico J. Viquerat A. Larcher R. Valette A. F. Sanches V. Lannelongue H. Ghraieb R. Nemer Y. Ozpeynirci T. Liebig |
| author_sort | E. Hachem |
| collection | DOAJ |
| description | Abstract Developing new capabilities to predict the risk of intracranial aneurysm rupture and to improve treatment outcomes in the follow-up of endovascular repair is of tremendous medical and societal interest, both to support decision-making and assessment of treatment options by medical doctors, and to improve the life quality and expectancy of patients. This study aims at identifying and characterizing novel flow-deviator stent devices through a high-fidelity computational framework that combines state-of-the-art numerical methods to accurately describe the mechanical exchanges between the blood flow, the aneurysm, and the flow-deviator and deep reinforcement learning algorithms to identify a new stent concepts enabling patient-specific treatment via accurate adjustment of the functional parameters in the implanted state. |
| first_indexed | 2024-04-09T14:02:26Z |
| format | Article |
| id | doaj.art-a51b4a02a8264d78b234903e10039a6e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T14:02:26Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a51b4a02a8264d78b234903e10039a6e2023-05-07T11:13:08ZengNature PortfolioScientific Reports2045-23222023-05-0113111610.1038/s41598-023-34007-zReinforcement learning for patient-specific optimal stenting of intracranial aneurysmsE. Hachem0P. Meliga1A. Goetz2P. Jeken Rico3J. Viquerat4A. Larcher5R. Valette6A. F. Sanches7V. Lannelongue8H. Ghraieb9R. Nemer10Y. Ozpeynirci11T. Liebig12MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635Department of Neuroradiology, University Hospital Munich (LMU)MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635Department of Neuroradiology, University Hospital Munich (LMU)Department of Neuroradiology, University Hospital Munich (LMU)Abstract Developing new capabilities to predict the risk of intracranial aneurysm rupture and to improve treatment outcomes in the follow-up of endovascular repair is of tremendous medical and societal interest, both to support decision-making and assessment of treatment options by medical doctors, and to improve the life quality and expectancy of patients. This study aims at identifying and characterizing novel flow-deviator stent devices through a high-fidelity computational framework that combines state-of-the-art numerical methods to accurately describe the mechanical exchanges between the blood flow, the aneurysm, and the flow-deviator and deep reinforcement learning algorithms to identify a new stent concepts enabling patient-specific treatment via accurate adjustment of the functional parameters in the implanted state.https://doi.org/10.1038/s41598-023-34007-z |
| spellingShingle | E. Hachem P. Meliga A. Goetz P. Jeken Rico J. Viquerat A. Larcher R. Valette A. F. Sanches V. Lannelongue H. Ghraieb R. Nemer Y. Ozpeynirci T. Liebig Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms Scientific Reports |
| title | Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms |
| title_full | Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms |
| title_fullStr | Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms |
| title_full_unstemmed | Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms |
| title_short | Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms |
| title_sort | reinforcement learning for patient specific optimal stenting of intracranial aneurysms |
| url | https://doi.org/10.1038/s41598-023-34007-z |
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