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Performance of a deep learning tool to detect missed aortic dilatation in a large chest CT cohort.
Pradella, Maurice; Achermann, Rita; Sperl, Jonathan I; Kärgel, Rainer; Rapaka, Saikiran; Cyriac, Joshy; Yang, Shan; Sommer, Gregor; Stieltjes, Bram; Bremerich, Jens; Brantner, Philipp; Sauter, Alexander W.
Afiliación
  • Pradella M; Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Achermann R; Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
  • Sperl JI; Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Kärgel R; Siemens Healthineers, Forchheim, Germany.
  • Rapaka S; Siemens Healthineers, Forchheim, Germany.
  • Cyriac J; Siemens Healthineers, Princeton, NJ, United States.
  • Yang S; Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Sommer G; Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Stieltjes B; Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Bremerich J; Hirslanden Klinik St. Anna, Luzern, Switzerland.
  • Brantner P; Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Sauter AW; Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
Front Cardiovasc Med ; 9: 972512, 2022.
Article en En | MEDLINE | ID: mdl-36072871
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Cardiovasc Med Año: 2022 Tipo del documento: Article País de afiliación: Suiza
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Cardiovasc Med Año: 2022 Tipo del documento: Article País de afiliación: Suiza