Machine Learning Classification of Cerebral Aneurysm Rupture Status with Morphologic Variables and Hemodynamic Parameters

PURPOSE: To construct a classification model of rupture status and to clarify the importance of morphologic variables and hemodynamic parameters on rupture status by applying a machine learning (ML) algorithm to morphologic and hemodynamic data of cerebral aneurysms. MATERIALS AND METHODS: Between 2...

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Detalhes bibliográficos
Publicado no:Radiol Artif Intell
Main Authors: Tanioka, Satoru, Ishida, Fujimaro, Yamamoto, Atsushi, Shimizu, Shigetoshi, Sakaida, Hiroshi, Toyoda, Mitsuru, Kashiwagi, Nobuhisa, Suzuki, Hidenori
Formato: Artigo
Idioma:Inglês
Publicado em: Radiological Society of North America 2020
Assuntos:
Original Research
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC8017392/
https://ncbi.nlm.nih.gov/pubmed/33937812
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1148/ryai.2019190077
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