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Related Concept Videos

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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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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Updated: Jun 2, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational

Lovedeep S Dhingra1,2, Arya Aminorroaya1,2, Veer Sangha1,2,3

  • 1Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA.

European Heart Journal
|January 13, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) applied to electrocardiogram (ECG) images can predict heart failure (HF) risk. This AI-ECG model identifies individuals at significantly higher risk, serving as a novel digital biomarker for HF stratification.

Keywords:
Cardiovascular screeningDeep learningElectrocardiogramsHeart failurePredictive modelling

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomarkers

Background:

  • Current heart failure (HF) risk stratification relies on comprehensive clinical evaluations.
  • Novel, accessible methods are needed to predict HF risk more effectively.

Purpose of the Study:

  • To evaluate artificial intelligence (AI) applied to electrocardiogram (ECG) images as a strategy for predicting HF risk.
  • To assess the performance of an AI-ECG model in identifying individuals at high risk for incident HF.

Main Methods:

  • An AI-ECG model was developed to detect left ventricular systolic dysfunction from 12-lead ECG images.
  • The model's association with incident HF was evaluated across multinational cohorts (YNHHS, UKB, ELSA-Brasil).
  • Model discrimination was assessed using Harrell's C-statistic and compared with Pooled Cohort Equations to Prevent HF (PCP-HF).

Main Results:

  • A positive AI-ECG screen was associated with a 4- to 24-fold higher risk of new-onset HF across cohorts.
  • The association remained consistent after adjusting for comorbidities and competing risks.
  • Model discrimination ranged from 0.718 to 0.810, with significant improvements when AI-ECG was combined with PCP-HF.

Conclusions:

  • An AI model utilizing a single ECG image can effectively predict future HF risk.
  • AI-ECG serves as a promising digital biomarker for stratifying HF risk.
  • This approach offers a potentially simpler and more accessible method for HF risk assessment.