Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection.

Journal: Clinical imaging
Published Date: Jul 30, 2024

Abstract

PURPOSE: Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and clinical utility of an artificial intelligence (AI)-based application designed to assist clinicians in the detection of PE on CT pulmonary angiography (CTPA).

Authors

  • Angela Ayobi
    Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
  • Peter D Chang
    Department of Radiological Sciences and Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Orange, California.
  • Daniel S Chow
    Center for Artificial Intelligence in Diagnostic Medicine (CAIDM) and the University of California School of Medicine-Irvine, Irvine, CA.
  • Brent D Weinberg
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Suite D112, Atlanta, GA, 30322, USA.
  • Maxime Tassy
    Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
  • Angelo Franciosini
    Aix Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille, France.
  • Marlene Scudeler
    Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
  • Sarah Quenet
    Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
  • Christophe Avare
    Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
  • Yasmina Chaibi
    Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.

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