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Diagnosing Pulmonary EmbolismDiagnosing pulmonary embolism (PE) involves clinical assessment and advanced imaging tests. The preferred diagnostic tool is the spiral (helical) CT scan or CT angiography (CTA), which uses intravenous contrast media to visualize the pulmonary vasculature and identify emboli.A ventilation-perfusion (V/Q) scan is an alternative for patients unable to receive contrast media. This scan includes both perfusion and ventilation scanning. Perfusion scanning involves...
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Deep Learning-based Automated Detection of Pulmonary Embolism: Is It Reliable?

Önder Babacan1, Ahmet Yasin Karkaş1, Görkem Durak1

  • 1Department of Radiology, Istanbul Medical Faculty, Istanbul University, Istanbul, Turkey.

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Summary
This summary is machine-generated.

This study found that the Canon Automation Platform AI accurately detects pulmonary embolisms (PEs) in chest CT pulmonary angiograms (CTPAs), improving clinical workflow. This AI tool offers valuable diagnostic support for acute PE, especially where specialist radiologists are unavailable.

Keywords:
artificial intelligence (AI)computed tomography pulmonary angiography (CTPA)pulmonary embolism (PE)

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Pulmonary embolism (PE) diagnosis relies heavily on chest computed tomography pulmonary angiograms (CTPAs).
  • Automated detection of PE using artificial intelligence (AI) can potentially enhance diagnostic efficiency and accuracy.
  • The Canon Automation Platform is an AI tool designed for automated PE detection in CTPAs.

Purpose of the Study:

  • To evaluate the diagnostic accuracy and clinical utility of the Canon Automation Platform for automated PE detection and localization in CTPAs.
  • To assess the performance of the AI program across all levels of pulmonary arteries.

Main Methods:

  • Retrospective analysis of 1474 CTPAs with suspected PEs.
  • Evaluation by senior radiology residents and verification by experienced thoracic radiologists.
  • Integration and assessment of the Canon Automation Platform's diagnostic performance with PACS.

Main Results:

  • The AI platform demonstrated high diagnostic accuracy, with AUC-ROC scores ranging from 0.945 to 0.996.
  • Overall accuracy was high (95.4%–99.7%), with excellent specificity (98.7%–100%).
  • Sensitivity was slightly lower in subsegmental branches (81.4%–84.7%) but remained high overall.

Conclusions:

  • The Canon Automation Platform effectively detects PE in CTPAs using deep learning, streamlining clinical workflows.
  • AI offers robust diagnostic support for acute PE, particularly beneficial for institutions lacking 24/7 radiology specialist access.
  • The AI tool shows significant promise in improving the management of patients with suspected pulmonary embolism.