Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.

Journal: PLoS medicine
Published Date: Nov 27, 2018

Abstract

BACKGROUND: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation.

Authors

  • Nicholas Bien
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Pranav Rajpurkar
    Harvard Medical School, Department of Biomedical Informatics, Cambridge, MA, 02115, US.
  • Robyn L Ball
    From the Department of Radiology, Stanford University School of Medicine, Stanford University Medical Center, 725 Welch Rd, Room 1675, Stanford, Calif 94305-5913 (M.C.C., N.M., D.B.L., C.P.L., M.P.L.); Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, Calif (R.L.B., L.Y.); Department of Bioinformatics, University of Utah Medical Center, Salt Lake City, Utah (B.E.C.); and Department of Radiology, Duke University Medical Center, Durham, NC (T.J.A.).
  • Jeremy Irvin
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Allison Park
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Erik Jones
    Inspire, Arlington, Virginia.
  • Michael Bereket
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Bhavik N Patel
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Kristen W Yeom
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Katie Shpanskaya
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Safwan Halabi
    Department of Radiology, Stanford University, Palo Alto, California.
  • Evan Zucker
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Gary Fanton
    Department of Orthopedic Surgery, Stanford University, Stanford, California, United States of America.
  • Derek F Amanatullah
    Department of Orthopedic Surgery, Stanford University, Stanford, California, United States of America.
  • Christopher F Beaulieu
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Geoffrey M Riley
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Russell J Stewart
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Francis G Blankenberg
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • David B Larson
    Department of Radiology, Warren Alpert Medical School, Brown University, 593 Eddy St, Providence, RI 02903 (I.P.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (I.P.); Visiana, Hørsholm, Denmark (H.H.T.); Department of Radiology, Stanford University, Palo Alto, Calif (S.S.H., D.B.L.); and Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.).
  • Ricky H Jones
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Curtis P Langlotz
    Stanford University, University Medical Line, Stanford, CA, 94305, US.
  • Andrew Y Ng
  • Matthew P Lungren

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