Your browser doesn't support javascript.
loading
Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges.
Weikert, Thomas; Francone, Marco; Abbara, Suhny; Baessler, Bettina; Choi, Byoung Wook; Gutberlet, Matthias; Hecht, Elizabeth M; Loewe, Christian; Mousseaux, Elie; Natale, Luigi; Nikolaou, Konstantin; Ordovas, Karen G; Peebles, Charles; Prieto, Claudia; Salgado, Rodrigo; Velthuis, Birgitta; Vliegenthart, Rozemarijn; Bremerich, Jens; Leiner, Tim.
Afiliação
  • Weikert T; Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland. thomas.weikert@usb.ch.
  • Francone M; Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, V.le Regina Elena 324, 00161, Rome, Italy.
  • Abbara S; Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9316, USA.
  • Baessler B; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
  • Choi BW; Radiology Department, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
  • Gutberlet M; Department of Diagnostic and Interventional Radiology, Heart Center Leipzig - University Leipzig, Strümpellstrasse 39, 04289, Leipzig, Germany.
  • Hecht EM; Department of Radiology, Weill Cornell Medicine, 520 East 70th Street, New York, NY, 10021, USA.
  • Loewe C; Division of Cardiovascular and Interventional Radiology, Department of Bioimaging and Image-Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, A-1090, Vienna, Austria.
  • Mousseaux E; Department of Radiology, Hôpital Européen Georges Pompidou, APHP, University of Paris & INSERM, U970 29 rue Leblanc, 75015, Paris, France.
  • Natale L; Radiological and Haematological Sciences Department, Fondazione Policlinico Universitario A. Gemelli- IRCCS, Università Cattolica S. Cuore, Largo Agostino Gemelli 8, 00168, Rome, Italy.
  • Nikolaou K; Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe-Seyler-Strasse 3, 72076, Tübingen, Germany.
  • Ordovas KG; Department of Radiology and Biomedical Imaging, University of California- San Francisco, 505 Parnassus Ave, M396 Box 0628, San Francisco, CA, 94143-0628, USA.
  • Peebles C; Department of Radiology, University Hospital Southampton, Tremona Road, Southampton, SO16 6YD, UK.
  • Prieto C; School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, SE1 7EH, UK.
  • Salgado R; Department of Radiology, Antwerp University Hospital & Holy Heart Hospital Lier, Wilrijkstraat 10, 2650, Edegem, Belgium.
  • Velthuis B; Department of Radiology, Utrecht University Medical Center, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
  • Vliegenthart R; Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
  • Bremerich J; Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
  • Leiner T; Department of Radiology, Utrecht University Medical Center, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Eur Radiol ; 31(6): 3909-3922, 2021 Jun.
Article em En | MEDLINE | ID: mdl-33211147
Assuntos
Palavras-chave
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Aprendizado de Máquina Tipo de estudo: Clinical_trials / Diagnostic_studies / Guideline / Prognostic_studies Limite: Humans Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Suíça
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Aprendizado de Máquina Tipo de estudo: Clinical_trials / Diagnostic_studies / Guideline / Prognostic_studies Limite: Humans Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Suíça