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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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Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Introduction
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Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT

Lovedeep S Dhingra1, Arya Aminorroaya1, Veer Sangha2

  • 1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.

Journal of the American College of Cardiology
|March 26, 2025
PubMed
Summary
This summary is machine-generated.

An AI-ECG tool, PRESENT-SHD, effectively screens for structural heart diseases (SHDs) using electrocardiogram (ECG) images. This accessible technology aids in early detection and risk stratification for heart conditions.

Keywords:
artificial intelligencecardiovascular screeningdeep learningechocardiographyelectrocardiogramspredictive modelingstructural heart disease

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Area of Science:

  • Artificial Intelligence in Cardiology
  • Deep Learning for Medical Imaging
  • Cardiovascular Disease Screening

Background:

  • Early identification of structural heart diseases (SHDs) is crucial for disease management.
  • Cardiac imaging, essential for SHD diagnosis, faces accessibility limitations.

Purpose of the Study:

  • To develop an automated system for detecting and predicting multiple SHDs using 12-lead electrocardiogram (ECG) images.
  • To leverage an ensemble deep learning approach for enhanced diagnostic accuracy.

Main Methods:

  • Convolutional neural networks (CNNs) were developed to detect individual SHDs from ECG images.
  • An ensemble XGBoost model, PRESENT-SHD, was created for composite SHD screening.
  • Validation was performed across multiple hospitals, a population-based cohort (ELSA-Brasil), and UK Biobank.

Main Results:

  • PRESENT-SHD achieved an AUROC of 0.886 in the test set, with 90% sensitivity and 66% specificity.
  • The model demonstrated consistent performance across diverse demographic subgroups and ECG formats, including smartphone images.
  • A positive PRESENT-SHD screen indicated a 2- to 4-fold increased risk of new-onset SHD/heart failure.

Conclusions:

  • PRESENT-SHD is a validated AI-ECG tool for identifying a range of SHDs from ECG images.
  • This approach offers a robust, scalable, and accessible method for automated SHD screening.
  • The tool facilitates effective risk stratification for cardiovascular conditions.