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

Imaging Studies for Cardiovascular System III: X-Ray

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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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Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

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Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
492

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Related Experiment Video

Updated: Sep 16, 2025

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
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CELM: An Ensemble Deep Learning Model for Early Cardiomegaly Diagnosis in Chest Radiography.

Erdem Yanar1,2, Fırat Hardalaç2, Kubilay Ayturan2

  • 1Department of Healthcare Systems System Engineering, ASELSAN, 06200 Ankara, Turkey.

Diagnostics (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

This study developed a deep learning model for automated cardiomegaly detection from chest X-rays. The Combined Ensemble Learning Model (CELM) achieved high accuracy, showing potential for clinical decision support.

Keywords:
automated diagnosiscardiomegalyclinical decision supportdeep learningearly diagnosisensemble learningmedical imaging

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Cardiology

Background:

  • Cardiomegaly, an enlarged heart, is a critical radiological sign of cardiovascular disease.
  • Early detection of cardiomegaly is crucial for timely intervention and improved patient outcomes.
  • Automated diagnosis using deep learning on chest X-rays (CXRs) offers a promising approach.

Purpose of the Study:

  • To investigate the efficacy of deep learning models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), for automated cardiomegaly diagnosis from CXRs.
  • To develop and evaluate a novel ensemble model for enhanced diagnostic performance.

Main Methods:

  • A large, diverse dataset of posteroanterior (PA) CXR images was compiled from multiple sources (PadChest, NIH CXR, VinDr-CXR, CheXpert).
  • Various pre-trained CNN architectures (VGG16, ResNet50, InceptionV3, DenseNet121, DenseNet201, AlexNet) and Vision Transformer models were trained and compared.
  • A stacking-based ensemble model, the Combined Ensemble Learning Model (CELM), was introduced, integrating CNN features via a meta-classifier.

Main Results:

  • The CELM demonstrated superior diagnostic performance with 92% accuracy, 99% precision, 89% recall, 0.94 F1-score, 92.0% specificity, and 0.90 AUC.
  • The model showed high agreement with expert annotations, indicating its potential for reliable clinical application.
  • Vision Transformers provided competitive results, suggesting their utility as complementary tools to CNNs.

Conclusions:

  • The proposed CELM framework shows potential as an efficient and scalable decision-support tool for cardiomegaly screening.
  • Further validation is recommended for its implementation in resource-limited settings like ICUs and EDs.
  • Automated deep learning approaches can significantly aid in rapid and accurate cardiomegaly diagnosis.