Meta-analysis of AI-based pulmonary embolism detection: How reliable are deep learning models?

Journal: Computers in biology and medicine
Published Date: May 23, 2025

Abstract

RATIONALE AND OBJECTIVES: Deep learning (DL)-based methods show promise in detecting pulmonary embolism (PE) on CT pulmonary angiography (CTPA), potentially improving diagnostic accuracy and workflow efficiency. This meta-analysis aimed to (1) determine pooled performance estimates of DL algorithms for PE detection; and (2) compare the diagnostic efficacy of convolutional neural network (CNN)- versus U-Net-based architectures.

Authors

  • Ezio Lanza
    Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini, 4, Pieve Emanuele MI 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy. Electronic address: ezio.lanza@humanitas.it.
  • Angela Ammirabile
    Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini, 4, Pieve Emanuele MI 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy.
  • Marco Francone
    Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, V.le Regina Elena 324, 00161, Rome, Italy.

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