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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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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Imaging Studies for Cardiovascular System III: X-Ray01:20

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Related Experiment Video

Updated: Jul 21, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
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Published on: April 12, 2024

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Hybrid Classical-Quantum Transfer Learning for Cardiomegaly Detection in Chest X-rays.

Pierre Decoodt1, Tan Jun Liang2,3, Soham Bopardikar4

  • 1Cardiologie, Centre Hospitalo-Universitaire Brugmann, Faculté de Médecine, Université Libre de Bruxelles, Place Van Gehuchten 4, 1020 Brussels, Belgium.

Journal of Imaging
|July 28, 2023
PubMed
Summary
This summary is machine-generated.

Quantum machine learning models show promise for detecting cardiomegaly (enlarged heart) in chest X-rays (CXRs). These hybrid classical-quantum models achieved high accuracy, rivaling traditional methods and improving visualization for healthcare professionals.

Keywords:
cardiomegalycardiovascular diseaseschest X-raydiagnosisheart failuremachine learningmedical imagingquantum computingtransfer learningvisualization

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Area of Science:

  • Quantum computing applications in healthcare
  • Machine learning for medical image analysis
  • Cardiovascular disease diagnostics

Background:

  • Cardiovascular diseases pose significant health challenges.
  • Chest X-rays (CXRs) are crucial for diagnosing conditions like cardiomegaly.
  • Quantum machine learning (QML) offers potential advancements in medical imaging analysis.

Purpose of the Study:

  • To develop and evaluate hybrid classical-quantum (CQ) transfer learning models for detecting cardiomegaly in CXRs.
  • To compare the performance of CQ models against classical-classical (CC) models.
  • To assess the interpretability of CQ models using heatmaps.

Main Methods:

  • Designed hybrid CQ transfer learning models integrating parameterized quantum circuits (using Qiskit and PennyLane) with classical networks (PyTorch).
  • Utilized a balanced dataset of 2436 posteroanterior CXRs from the CheXpert repository.
  • Employed k-fold cross-validation and a state vector simulator for training CQ models.
  • Analyzed trainability using normalized global effective dimension.

Main Results:

  • Achieved high predictive performance with ROC AUC scores up to 0.93 and accuracies up to 0.87, comparable to CC models.
  • Demonstrated significantly more frequent visualization of trustworthy Grad-CAM++ heatmaps covering the heart with CQ models (94%) compared to CC models (61%).

Conclusions:

  • Hybrid classical-quantum models show strong potential for accurate cardiomegaly detection in CXRs.
  • The enhanced interpretability of CQ models may increase their adoption by healthcare professionals.
  • QML represents a promising frontier for improving cardiovascular diagnostics through medical imaging.