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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 IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Imaging Studies for Cardiovascular System I:Echocardiography01:17

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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.
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Assessing Blood pressure using a doppler ultrasound01:19

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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
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Related Experiment Video

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Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network.

Mohammad Mahbubur Rahman Khan Mamun1, Tarek Elfouly1

  • 1Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA.

Bioengineering (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

A new hybrid one-dimensional convolutional neural network (1D CNN) offers improved accuracy for detecting heart disease compared to traditional methods. This artificial intelligence approach shows promise for earlier and more reliable heart condition diagnosis.

Keywords:
1D CNNartificial intelligencediagnosisfeature selectionheart disease

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Area of Science:

  • Cardiology and Artificial Intelligence
  • Biomedical Data Analysis

Background:

  • Heart disease poses a significant public health challenge, necessitating early detection for effective management.
  • Current diagnostic methods are often inconvenient, costly, and unsuitable for frequent monitoring.
  • Emerging artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), show potential for improved heart condition detection.

Purpose of the Study:

  • To develop a more accurate and reliable AI model for detecting heart disease.
  • To address limitations of existing datasets, such as small size and imbalance.
  • To evaluate the performance of a novel hybrid deep learning model against established ML algorithms.

Main Methods:

  • A hybrid one-dimensional convolutional neural network (1D CNN) was developed.
  • A large dataset from online surveys was utilized, with feature selection algorithms employed.
  • The 1D CNN model was trained and validated on heart disease data, including non-coronary heart disease (no-CHD) and coronary heart disease (CHD).

Main Results:

  • The proposed 1D CNN model demonstrated superior accuracy compared to artificial neural networks, random forest, AdaBoost, and support vector machine algorithms.
  • Validation accuracy for non-coronary heart disease (no-CHD) was 80.1%, and for coronary heart disease (CHD) was 76.9%.
  • The 1D CNN exhibited better overall performance, including accuracy, false negative rates, and false positive rates, across multiple heart conditions.

Conclusions:

  • The hybrid 1D CNN model represents a significant advancement in AI-driven heart disease detection.
  • This approach offers improved accuracy and reliability for diagnosing various heart conditions.
  • The findings suggest potential for more efficient and effective early detection of heart disease in clinical settings.