Deep Convolutional Neural Network-Based Diagnosis of Anterior Cruciate Ligament Tears: Performance Comparison of Homogenous Versus Heterogeneous Knee MRI Cohorts With Different Pulse Sequence Protocols and 1.5-T and 3-T Magnetic Field Strengths.

Journal: Investigative radiology
Published Date: Aug 1, 2020

Abstract

OBJECTIVES: The aim of this study was to clinically validate a Deep Convolutional Neural Network (DCNN) for the detection of surgically proven anterior cruciate ligament (ACL) tears in a large patient cohort and to analyze the effect of magnetic resonance examinations from different institutions, varying protocols, and field strengths.

Authors

  • Christoph Germann
    Department of Radiology, Balgrist University Hospital
  • Giuseppe Marbach
    Balzano Informatik AG, Zurich, Switzerland.
  • Francesco Civardi
    Balzano Informatik AG, Zurich, Switzerland.
  • Sandro F Fucentese
    Faculty of Medicine, University of Zurich, Zurich, Switzerland.
  • Jan Fritz
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA. jfritz9@jhmi.edu.
  • Reto Sutter
    Department of Radiology, Balgrist University Hospital
  • Christian W A Pfirrmann
    Department of Radiology, Balgrist University Hospital, Forchstrasse 340, CH-8008, Zurich, Switzerland.
  • Benjamin Fritz
    Department of Radiology, Balgrist University Hospital, Forchstrasse 340, CH-8008, Zurich, Switzerland. benjamin.fritz@balgrist.ch.

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