Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment.

Journal: Open heart
PMID: 39800437

Abstract

BACKGROUND: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT).

Authors

  • Rachel Bernardo
    Division of Cardiology and Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA.
  • Nick S Nurmohamed
    Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
  • Michiel J Bom
    Department of Cardiology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Ruurt Jukema
    Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
  • Ruben W de Winter
    Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
  • Ralf Sprengers
    Department of Radiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
  • Erik S G Stroes
    Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
  • James K Min
    3 Department of Radiology, Weill Cornell Medicine , New York, New York.
  • James Earls
    Department of Radiology, George Washington University Hospital, 900 23rd St NW, Washington, DC, 20037, USA.
  • Ibrahim Danad
    Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands.
  • Andrew D Choi
    Division of Cardiology and Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA adchoi@mfa.gwu.edu.
  • Paul Knaapen
    Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands.

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