Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.

Journal: European radiology
Published Date:

Abstract

PURPOSE: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload.

Authors

  • Alejandro Rodríguez-Ruiz
    From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.).
  • Kristina Lång
    Institute for Biomedical Engineering, ETH Zurich, Gloriastrasse 35, 8092, Zürich, Switzerland.
  • Albert Gubern-Mérida
    Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Jonas Teuwen
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
  • Mireille Broeders
    Department for Health Evidence, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
  • Gisella Gennaro
    Veneto Institute of Oncology (IOV)-IRCCS, via Gattamelata 64, 35128, Padua, Italy.
  • Paola Clauser
    Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria.
  • Thomas H Helbich
    Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy.
  • Margarita Chevalier
    Medical Physics Group, Radiology Department, Faculty of Medicine, Universidad Complutense de Madrid, Pza. Ramón y Cajal s/n, 28040, Madrid, Spain.
  • Thomas Mertelmeier
    Siemens Healthcare GmbH, Diagnostic Imaging, X-Ray Products, Technology & Concepts, Siemensstr. 3, 91301, Forchheim, Germany.
  • Matthew G Wallis
    Cambridge Breast Unit and NIHR Biomedical Research Unit, Box 97, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
  • Ingvar Andersson
    Unilabs Breast Center, Skåne University Hospital, Jan Waldenströms gata 22, SE-20502, Malmö, Sweden.
  • Sophia Zackrisson
    Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden.
  • Ioannis Sechopoulos
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
  • Ritse M Mann
    Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands.