Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time.

Journal: AJNR. American journal of neuroradiology
PMID: 39255988

Abstract

BACKGROUND AND PURPOSE: ASPECTS is a long-standing and well-documented selection criterion for acute ischemic stroke treatment; however, the interpretation of ASPECTS is a challenging and time-consuming task for physicians with notable interobserver variabilities. We conducted a multireader, multicase study in which readers assessed ASPECTS without and with the support of a deep learning (DL)-based algorithm to analyze the impact of the software on clinicians' performance and interpretation time.

Authors

  • Angela Ayobi
    Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
  • Adam Davis
    Amalgamated Vision (A.D.), Brentwood, Tennessee.
  • 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.
  • Kambiz Nael
    Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA.
  • Maxime Tassy
    Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
  • Sarah Quenet
    Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
  • Sylvain Fogola
    From Avicenna.AI (A.A., M.T., S.Q., S.F., C.A., Y.C.), La Ciotat, France.
  • Peter Shabe
    Advance Research Associates (P.S.), Santa Clara, California.
  • David Fussell
    Department of Radiological Sciences (P.D.C., D.S.C., D.F.), University of California Irvine, Orange, California.
  • 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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