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Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.
Yang, Wenjie; Chen, Chihua; Yang, Yanzhao; Chen, Lei; Yang, Changwei; Gong, Lianggeng; Wang, Jianing; Shi, Feng; Wu, Dijia; Yan, Fuhua.
Afiliação
  • Yang W; Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Chen C; Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yang Y; Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Chen L; Department of Radiology, Peking University People's Hospital, Beijing, China.
  • Yang C; Department of Radiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
  • Gong L; Department of Radiology, Second Affiliated Hospital of Nanchang University, Nanchang, China.
  • Wang J; Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China.
  • Shi F; Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
  • Wu D; Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
  • Yan F; Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. yfh11655@rjh.com.cn.
Radiol Med ; 128(3): 307-315, 2023 Mar.
Article em En | MEDLINE | ID: mdl-36800112
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença da Artéria Coronariana / Estenose Coronária / Aprendizado Profundo Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Radiol Med Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença da Artéria Coronariana / Estenose Coronária / Aprendizado Profundo Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Radiol Med Ano de publicação: 2023 Tipo de documento: Article