Leveraging Artificial Intelligence to Enhance Peer Review: Missed Liver Lesions on Computed Tomographic Pulmonary Angiography.

Journal: Journal of the American College of Radiology : JACR
Published Date: Sep 17, 2022

Abstract

PURPOSE: The aim of this study was to use artificial intelligence (AI) to facilitate peer review for detection of missed suspicious liver lesions (SLLs) on CT pulmonary angiographic (CTPA) examinations.

Authors

  • Sarah P Thomas
    Department of Radiology, Duke University Medical Center, Durham, North Carolina. Electronic address: sarah.p.thomas@duke.edu.
  • Tyler J Fraum
    From the Mallinckrodt Institute of Radiology.
  • Lawrence Ngo
    Department of Radiology, Duke University Medical Center, Durham, North Carolina.
  • Robert Harris
    Virtual Radiologic, Eden Prairie, Minnesota.
  • Elie Balesh
    Medical Director, Ferrum Health, Sunnyvale, California. Electronic address: https://twitter.com/ElieBalesh.
  • Mustafa R Bashir
    Department of Radiology, Duke University, Durham, North Carolina, USA.
  • Benjamin Wildman-Tobriner
    From the Department of Radiology, Duke University Hospital, 2301 Erwin Rd, Durham, NC 27701 (B.W.T., M.B., J.K.H., R.G.S., M.A.M.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (W.D.M., D.T.); and Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (F.N.T.).

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