Comparing efficiency of an attention-based deep learning network with contemporary radiological workflow for pulmonary embolism detection on CTPA: A retrospective study.

Journal: European journal of radiology open
Published Date: Jun 1, 2025

Abstract

RATIONAL AND OBJECTIVES: Pulmonary embolism (PE) is the third most fatal cardiovascular disease in the United States. Currently, Computed Tomography Pulmonary Angiography (CTPA) serves as diagnostic gold standard for detecting PE. However, its efficacy is limited by factors such as contrast bolus timing, physician-dependent diagnostic accuracy, and time taken for scan interpretation. To address these limitations, we propose an AI-based PE triaging model (AID-PE) designed to predict the presence and key characteristics of PE on CTPA. This model aims to enhance diagnostic accuracy, efficiency, and the speed of PE identification.

Authors

  • Gagandeep Singh
    Department of Chemistry & Biochemistry, The University of Mississippi, University, MS 38677, United States.
  • Annie Singh
    Atal Bihari Vajpayee Institute of Medical Sciences, New Delhi, India.
  • Tejasvi Kainth
    Department of Psychiatry, BronxCare Health System, NY, USA.
  • Sudhir Suman
    Department of Biomedical Informatics, Stony Brook University, USA.
  • Nicole Sakla
    Department of Radiology, Newark Beth Israel Medical Center, NJ, USA.
  • Luke Partyka
    Department of Radiology, Newark Beth Israel Medical Center, NJ, USA.
  • Tej Phatak
    Department of Radiology, Rutgers-Newark Beth Israel Medical Center, NJ, USA.
  • Prateek Prasanna
    Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, United States.

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