Quality assessment of expedited AI generated reformatted images for ED acquired CT abdomen and pelvis imaging.

Journal: Abdominal radiology (New York)
Published Date: Sep 18, 2024

Abstract

PURPOSE: Retrospectively compare image quality, radiologist diagnostic confidence, and time for images to reach PACS for contrast enhanced abdominopelvic CT examinations created on the scanner console by technologists versus those generated automatically by thin-client artificial intelligence (AI) mechanisms.

Authors

  • Daniel Freedman
  • Barun Bagga
    Department of Radiology, NYU Langone Health, New York, New York, USA.
  • Kira Melamud
    Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA.
  • Thomas O'Donnell
    Siemens Medical Solutions USA, Inc, 40 Liberty Boulevard, Malvern, PA, 19355, USA.
  • Emilio Vega
    New York University Langone Medical Center, New York, USA.
  • Malte Westerhoff
    Visage Imaging (Germany), Berlin, Germany.
  • Bari Dane
    NYU Langone Health Department of Radiology, 660 1st Avenue, New York, NY 10016 (B.D., B.B., S.B., S.K., A.R., F.F., H.C.); NYU Long Island Department of Radiology, Mineola, NY 11501 (B.D., B.B., B.B., S.B., A.R., F.F., M.K., H.C.). Electronic address: Bari.Dane@nyulangone.org.

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