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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,...
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Imaging Studies for Cardiovascular System II:Types of Echocardiography01:20

Imaging Studies for Cardiovascular System II:Types of Echocardiography

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Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
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Correlation between ECG and Cardiac Cycle01:25

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Electrophysiology of Normal Cardiac Rhythm01:19

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The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
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Related Experiment Video

Updated: Jul 28, 2025

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
05:32

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

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Cardiac phase detection in echocardiography using convolutional neural networks.

Moomal Farhad1, Mohammad Mehedy Masud2, Azam Beg1

  • 1College of Information Technology, United Arab Emirates University, Al Ain, P.O. Box 15551, United Arab Emirates.

Scientific Reports
|June 1, 2023
PubMed
Summary

DeepPhase, a deep learning model, automates cardiac phase detection in echocardiography, improving accuracy and efficiency for cardiologists. This AI tool identifies end-systolic (ES) and end-diastolic (ED) phases, reducing manual errors in heart condition assessments.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Echocardiography is crucial for assessing heart conditions, relying on accurate identification of end-systolic (ES) and end-diastolic (ED) cardiac phases.
  • Manual detection of these phases is time-consuming and prone to diagnostic errors, necessitating automated solutions.
  • Non-essential frames (Non-ESED) can complicate manual phase identification.

Purpose of the Study:

  • To develop and evaluate DeepPhase, a deep learning model for automated detection of ES, ED, and Non-ESED cardiac phases in echocardiography.
  • To improve the efficiency and accuracy of cardiac phase identification, thereby minimizing diagnostic errors.
  • To provide a tool that assists cardiology personnel in analyzing echocardiographic images.

Main Methods:

  • A convolutional neural network (CNN) model, DeepPhase, was developed using echocardiography images.
  • The model was trained to identify ES, ED, and Non-ESED phases without requiring left ventricle segmentation or electrocardiograms.
  • A novel cropping technique was introduced to enhance model performance and real-world applicability.

Main Results:

  • DeepPhase demonstrated superior performance compared to existing techniques.
  • The model achieved an area under the curve (AUC) of 0.96 on the CAMUS dataset.
  • An AUC of 0.82 was achieved on the newly collected CardiacPhase dataset.

Conclusions:

  • DeepPhase offers an automated and efficient method for cardiac phase detection in echocardiography.
  • The model shows significant potential for reducing diagnostic errors and improving workflow in cardiology.
  • The proposed deep learning approach and cropping technique are effective for real-world clinical applications.