Prospective Evaluation of AI Triage of Pulmonary Emboli on CT Pulmonary Angiograms.

Journal: Radiology
Published Date:

Abstract

Background Artificial intelligence (AI) algorithms have shown high accuracy for detection of pulmonary embolism (PE) on CT pulmonary angiography (CTPA) studies in academic studies. Purpose To determine whether use of an AI triage system to detect PE on CTPA studies improves radiologist performance or examination and report turnaround times in a clinical setting. Materials and Methods This prospective single-center study included adult participants who underwent CTPA for suspected PE in a clinical practice setting. Consecutive CTPA studies were evaluated in two phases, first by radiologists alone ( = 31) (May 2021 to June 2021) and then by radiologists aided by a commercially available AI triage system ( = 37) (September 2021 to December 2021). Sixty-two percent of radiologists (26 of 42 radiologists) interpreted studies in both phases. The reference standard was determined by an independent re-review of studies by thoracic radiologists and was used to calculate performance metrics. Diagnostic accuracy and turnaround times were compared using Pearson χ and Wilcoxon rank sum tests. Results Phases 1 and 2 included 503 studies (participant mean age, 54.0 years ± 17.8 [SD]; 275 female, 228 male) and 1023 studies (participant mean age, 55.1 years ± 17.5; 583 female, 440 male), respectively. In phases 1 and 2, 14.5% (73 of 503) and 15.9% (163 of 1023) of CTPA studies were positive for PE ( = .47). Mean wait time for positive PE studies decreased from 21.5 minutes without AI to 11.3 minutes with AI ( < .001). The accuracy and miss rate, respectively, for radiologist detection of any PE on CTPA studies was 97.6% and 12.3% without AI and 98.6% and 6.1% with AI, which was not significantly different ( = .15 and = .11, respectively). Conclusion The use of an AI triage system to detect any PE on CTPA studies improved wait times but did not improve radiologist accuracy, miss rate, or examination and report turnaround times. © RSNA, 2023 See also the editorial by Murphy and Tee in this issue.

Authors

  • Steven A Rothenberg
    Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, AL.
  • Cody H Savage
    From the Department of Radiology, University of Alabama at Birmingham, 619 S 19th St, Birmingham, AL 35233.
  • Asser Abou Elkassem
    From the Department of Radiology, University of Alabama at Birmingham, 619 S 19th St, Birmingham, AL 35233.
  • Satinder Singh
    Computer Science and Engineering Department, University of Michigan, Ann Arbor, Michigan 48109, USA.
  • Mostafa Abozeed
    From the Department of Radiology, University of Alabama at Birmingham, 619 S 19th St, Birmingham, AL 35233.
  • Omar Hamki
    From the Department of Radiology, University of Alabama at Birmingham, 619 S 19th St, Birmingham, AL 35233.
  • Kevin Junck
    From the Department of Radiology, University of Alabama at Birmingham, 619 S 19th St, Birmingham, AL 35233.
  • Srini Tridandapani
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia.
  • Mei Li
    Department of Laboratory Medicine, Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
  • Yufeng Li
    Department of Sports Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
  • Andrew D Smith
    From the University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35249.