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Related Concept Videos

Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care01:29

Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care

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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A blood clot, or thrombus, is a semi-solid mass composed of fibrin, platelets, and red blood cells. When it forms within a vessel, it can obstruct blood flow, known as thrombosis. If part of the clot detaches, it becomes an embolus that can travel and block distant vessels. When this occurs in the pulmonary arteries, it causes a condition known as pulmonary embolism (PE).Origin and ImpactMost often, the embolus originates from a thrombus in the deep veins of the lower limbs, a condition called...
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Related Experiment Video

Updated: May 26, 2026

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
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Published on: October 13, 2023

HUCSR-Net: Automated Pulmonary Embolism Detection Using 3D Deep Learning.

Cristian Velandia1, Hector Florez1

  • 1Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia.

F1000Research
|May 25, 2026
PubMed
Summary

A novel deep learning model, HUCSR-Net, demonstrates effective automated pulmonary embolism (PE) detection. This AI tool shows promise for improving diagnostic speed and accuracy in emergency settings, especially in resource-limited environments.

Keywords:
Convolutional Neural NetworkDeep LearningKinetics-400Pulmonary ThromboembolismPulmonary computed tomography angiographyPython

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Last Updated: May 26, 2026

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Published on: June 21, 2024

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Deep Learning

Background:

  • Pulmonary embolism (PE) is a significant cause of cardiovascular mortality and a diagnostic challenge.
  • Computed tomography pulmonary angiography (CTPA) is the standard but is time-consuming and requires expert interpretation.
  • Automated diagnostic tools are needed to expedite treatment and improve outcomes.

Purpose of the Study:

  • To develop and validate HUCSR-Net, an automated 3D convolutional neural network for PE detection.
  • To assess the model's performance using imaging studies from a single center.
  • To explore the feasibility of AI-driven PE detection in resource-constrained settings.

Main Methods:

  • Developed HUCSR-Net, a 3D convolutional neural network based on R(2 + 1)D-18 architecture.
  • Trained the model on 128,484 imaging studies from 86 patients using AdamW optimizer and binary cross-entropy loss.
  • Employed 5-fold cross-validation and evaluated performance using AUC, sensitivity, specificity, and F1-score.

Main Results:

  • The optimal model achieved a validation AUC of 0.7026, sensitivity of 0.8592, and F1-score of 0.592.
  • Demonstrated solid discriminatory performance with a Matthews correlation coefficient of 0.1515.
  • Results confirm the model's diagnostic capability despite a modest cohort size.

Conclusions:

  • A deep 3D convolutional network trained on a single-center cohort can achieve diagnostic performance comparable to large-scale studies.
  • HUCSR-Net shows feasibility for automated PE detection in resource-constrained environments.
  • The study supports integrating such AI systems as decision-support tools for radiologists.