Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms.

Autor: Hachem E; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France. elie.hachem@minesparis.psl.eu., Meliga P; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France., Goetz A; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France., Rico PJ; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France., Viquerat J; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France., Larcher A; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France., Valette R; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France., Sanches AF; Department of Neuroradiology, University Hospital Munich (LMU), Munich, Germany., Lannelongue V; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France., Ghraieb H; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France., Nemer R; MINES Paris, PSL Research University, Centre de mise en forme des matériaux (CEMEF), CNRS UMR 7635, 06904, Sophia Antipolis Cedex, France., Ozpeynirci Y; Department of Neuroradiology, University Hospital Munich (LMU), Munich, Germany., Liebig T; Department of Neuroradiology, University Hospital Munich (LMU), Munich, Germany.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 May 02; Vol. 13 (1), pp. 7147. Date of Electronic Publication: 2023 May 02.
DOI: 10.1038/s41598-023-34007-z
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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