Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms

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 im...

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Detalles Bibliográficos
Autores principales: Hachem, E., Meliga, P., Goetz, A., Rico, P. Jeken, Viquerat, J., Larcher, A., Valette, R., Sanches, A. F., Lannelongue, V., Ghraieb, H., Nemer, R., Ozpeynirci, Y., Liebig, T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154322/
https://www.ncbi.nlm.nih.gov/pubmed/37130900
http://dx.doi.org/10.1038/s41598-023-34007-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154322/
https://www.ncbi.nlm.nih.gov/pubmed/37130900
http://dx.doi.org/10.1038/s41598-023-34007-z

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