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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

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, evaluates...
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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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In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography
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EFNet: A multitask deep learning network for simultaneous quantification of left ventricle structure and function.

Samana Batool1, Imtiaz Ahmad Taj1, Mubeen Ghafoor2

  • 1Department of Electrical Engineering, Capital University of Science and Technology, Islamabad Expressway, Kahuta Road, Islamabad, 44000, Pakistan.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the EchoFused Network (EFNet), a deep learning model for automated left ventricle quantification from echocardiograms. EFNet accurately estimates ejection fraction and segmentations, improving cardiovascular diagnosis.

Keywords:
Cross-module fusionDeep learningEjection fractionHeart ultrasoundLV segmentation

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Area of Science:

  • Cardiovascular imaging and diagnostics
  • Artificial intelligence in medicine
  • Medical image analysis

Background:

  • Manual quantification of left ventricle structure and function from echocardiograms is time-consuming and prone to variability.
  • Accurate assessment of left ventricle function is crucial for diagnosing and managing cardiovascular diseases.
  • Automated methods are needed to improve the efficiency and reliability of echocardiogram analysis.

Purpose of the Study:

  • To develop a deep learning-based automated method for precise quantification of left ventricle structure and function from echocardiogram videos.
  • To eliminate the need for manual identification of end-systolic and end-diastolic frames, reducing potential inaccuracies.
  • To enhance the diagnosis and management of cardiovascular conditions through improved echocardiogram analysis.

Main Methods:

  • Introduction of the EchoFused Network (EFNet), a single, fully automated multitask deep learning network.
  • Simultaneous left ventricle segmentation and ejection fraction estimation using cross-module fusion.
  • Utilization of semi-supervised learning for ejection fraction estimation across the entire cardiac cycle, enhancing dependability.

Main Results:

  • The EFNet model demonstrated significant performance improvements on the EchoNet-Dynamic dataset.
  • Achieved a Mean Absolute Error (MAE) of 4.35% for ejection fraction estimation.
  • Obtained Dice Similarity Coefficient (DSC) values of 0.9309 (end-diastolic) and 0.9135 (end-systolic) for left ventricle segmentation.

Conclusions:

  • The study validates the efficacy of EFNet, a multitask deep learning network.
  • EFNet successfully quantifies left ventricle structure and function simultaneously using cross-module fusion.
  • The developed method offers a reliable and automated approach for echocardiogram analysis, aiding clinical practice.