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Article Dans Une Revue Scientific Reports Année : 2023

Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms

Résumé

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.
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Dates et versions

hal-04244723 , version 1 (16-10-2023)

Identifiants

Citer

E Hachem, P Meliga, A Goetz, P Jeken Rico, J Viquerat, et al.. Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms. Scientific Reports, 2023, 13, pp.7147. ⟨10.1038/s41598-023-34007-z⟩. ⟨hal-04244723⟩
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