Diagnostic Performance of Artificial Intelligence for Detection of Anterior Cruciate Ligament and Meniscus Tears: A Systematic Review.

Journal: Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
Published Date: Feb 1, 2021

Abstract

PURPOSE: To (1) determine the diagnostic efficacy of artificial intelligence (AI) methods for detecting anterior cruciate ligament (ACL) and meniscus tears and to (2) compare the efficacy to human clinical experts.

Authors

  • Kyle N Kunze
    Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA.
  • David M Rossi
    Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.
  • Gregory M White
    Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, U.S.A.
  • Aditya V Karhade
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Jie Deng
    Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, 1653 W. Congress Pkwy, Chicago, IL 60612, USA. Electronic address: Jie_deng@rush.edu.
  • Brady T Williams
    Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.
  • Jorge Chahla
    Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.. Electronic address: jachahla@msn.com.

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