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Related Concept Videos

Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care01:29

Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care

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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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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Pulmonary embolism (PE) occurs when a thrombus, fat or air embolus, amniotic fluid, or tumor tissue blocks one or more pulmonary arteries. These blockages originate in the venous system or the right side of the heart.EtiologyPE primarily arises from deep vein thrombosis (DVT) and other hypercoagulable states, such as inherited thrombophilias. Additional etiological factors include venous stasis, commonly seen in obesity, and endothelial injury from surgery and trauma. Less common causes include...
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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
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Related Experiment Video

Updated: Aug 3, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

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Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms.

Philippe A Grenier1, Angela Ayobi2, Sarah Quenet2

  • 1Department of Clinical Research and Innovation, Foch Hospital Suresnes, Versailles Saint Quentin University, 78000 Versailles, France.

Diagnostics (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

A deep learning algorithm accurately detects pulmonary embolism (PE) on chest CT angiograms, achieving 91.5% accuracy. This tool aids radiologists in urgent interpretations, potentially improving patient outcomes for PE diagnosis.

Keywords:
artificial intelligencechest CTcomputed tomography angiographydeep learning toolpulmonary embolism

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Prompt recognition and treatment of pulmonary embolism (PE) are critical for reducing mortality.
  • Chest computed tomography angiography (CTA) is a primary imaging modality for PE diagnosis.
  • Deep learning (DL) offers potential for automating image analysis in radiology.

Purpose of the Study:

  • To validate a DL-based algorithm for automated detection of PE on chest CTAs.
  • To assess the diagnostic performance of the DL algorithm in a real-world setting.
  • To evaluate the algorithm's ability to alert radiologists for urgent interpretation of potential PE cases.

Main Methods:

  • A hybrid 3D/2D UNet convolutional neural network (CNN) topology was employed.
  • The algorithm was trained on diverse datasets considering scanner vendors, patient age, and image acquisition parameters.
  • Validation was performed on 387 anonymized chest CTAs from multiple clinical sites, with ground truth established by three radiologists.

Main Results:

  • The DL algorithm demonstrated a sensitivity of 91.4% and specificity of 91.5% for PE detection.
  • Overall accuracy was 91.5%, with a balanced performance in identifying positive and negative PE cases.
  • False negatives were associated with chronic or subsegmental PEs, while false positives stemmed from artifacts and anatomical variations.

Conclusions:

  • The developed DL algorithm exhibits high diagnostic accuracy for PE detection on CTAs.
  • The algorithm shows balanced sensitivity and specificity, making it a promising tool for clinical use.
  • This technology can support radiologists in prioritizing urgent interpretations, potentially enhancing patient care for PE.