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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