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Rupture risk prediction of cerebral aneurysms using a novel convolutional neural network-based deep learning model.
Yang, Hyeondong; Cho, Kwang-Chun; Kim, Jung-Jae; Kim, Jae Ho; Kim, Yong Bae; Oh, Je Hoon.
Affiliation
  • Yang H; Department of Mechanical Engineering and BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan, Gyeonggi-do, Korea.
  • Cho KC; Department of Neurosurgery, College of Medicine, Yonsei University, Yongin Severance Hospital, Yongin, Korea.
  • Kim JJ; Department of Neurosurgery, College of Medicine, Yonsei University, Severance Hospital, Seoul, Korea.
  • Kim JH; Department of Neurosurgery, College of Medicine, Chosun University, Chosun University Hospital, Gwangju, Korea.
  • Kim YB; Department of Neurosurgery, College of Medicine, Yonsei University, Severance Hospital, Seoul, Korea jehoon@hanyang.ac.kr ybkim69@yuhs.ac.
  • Oh JH; Department of Mechanical Engineering and BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan, Gyeonggi-do, Korea jehoon@hanyang.ac.kr ybkim69@yuhs.ac.
J Neurointerv Surg ; 15(2): 200-204, 2023 Feb.
Article in En | MEDLINE | ID: mdl-35140167
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Intracranial Aneurysm / Aneurysm, Ruptured / Deep Learning Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: J Neurointerv Surg Year: 2023 Document type: Article Country of publication:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Intracranial Aneurysm / Aneurysm, Ruptured / Deep Learning Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: J Neurointerv Surg Year: 2023 Document type: Article Country of publication: