A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study.

Journal: Investigative radiology
Published Date: Oct 1, 2021

Abstract

OBJECTIVE: The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans.

Authors

  • David J Winkel
    From the Department of Radiology, University Hospital Basel, Basel, Switzerland.
  • Angela Tong
    Department of Radiology, NYU Langone Health, 660 1st Avenue, 3rd Floor, New York, NY, 10016, USA.
  • Bin Lou
    755 College Road East, Digital Technology and Innovation Division, Siemens Healthineers, Princeton, NJ, 08540.
  • Ali Kamen
    755 College Road East, Digital Technology and Innovation Division, Siemens Healthineers, Princeton, NJ, 08540.
  • Dorin Comaniciu
  • Jonathan A Disselhorst
    Siemens Healthcare AG Advanced Clinical Imaging Technology, Lausanne, Vaud, Switzerland.
  • 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.).
  • Henkjan Huisman
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Dieter Szolar
    Diagnostikum Graz Süd-West, Graz, Austria. Electronic address: dieter.szolar@diagnostikum-graz.at.
  • Ivan Shabunin
    Patero Clinic, Moscow, Russia. Electronic address: shabunin@pateroclinic.ru.
  • Moon Hyung Choi
    Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Pengyi Xing
    Department of Radiology, Changhai Hospital, Shanghai, China. Electronic address: 746992685@qq.com.
  • Tobias Penzkofer
    Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Robert Grimm
    Computational Linguistics & Psycholinguistics Research Center, Department of Linguistics, University of Antwerp, Antwerp, Belgium.
  • Heinrich von Busch
    Digital Health, Siemens Healthineers, Erlangen, Germany.
  • Daniel T Boll

Keywords

External Resources

Popular Topics
Recent Journals