دورية أكاديمية
Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms
العنوان: | Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms |
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المؤلفون: | 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 |
المصدر: | Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023) |
بيانات النشر: | Nature Portfolio, 2023. |
سنة النشر: | 2023 |
المجموعة: | LCC:Medicine LCC:Science |
مصطلحات موضوعية: | Medicine, Science |
الوصف: | 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. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 2045-2322 |
Relation: | https://doaj.org/toc/2045-2322 |
DOI: | 10.1038/s41598-023-34007-z |
URL الوصول: | https://doaj.org/article/a51b4a02a8264d78b234903e10039a6e |
رقم الأكسشن: | edsdoj.51b4a02a8264d78b234903e10039a6e |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 20452322 |
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DOI: | 10.1038/s41598-023-34007-z |