Application of artificial intelligence centric workflows for evaluation of neuroradiology emergencies.

Journal: Clinical imaging
Published Date:

Abstract

The goal of this study was to perform a pilot study to assess user-interface of radiologists with an artificial-intelligence (AI) centric workflow for detection of intracranial hemorrhage (ICH) and cervical spine fractures (CSFX). Over 12-month period, interaction and usage of AI software implemented in our institution, Aidoc, on head and cervical spine CT scans were obtained. Several interaction variables were defined, assessing different types of interaction between readers of different training level and AI software. The median usage of AI-centric workflow for detection of ICH and CSFX were 28.8% and 21.8%, respectively, demonstrating a significant additional engagement beyond Native workflow (worklist and PACS). Further studies are warranted to expand interaction assessments to further understand the value unlocked by the AI-centric workflows.

Authors

  • Delaram Shakoor
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
  • Khalid Al-Dasuqi
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT 06520-8042, United States of America. Electronic address: khalid.aldasuqi@yale.edu.
  • Joe Cavallo
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
  • Ichiro Ikuta
  • Syedmehdi Payabvash
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
  • Ajay Malhotra
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT, 06520-8042, USA. ajay.malhotra@yale.edu.