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Related Experiment Video

Updated: Aug 5, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and

Yu-Chang Huang1, Yu-Chun Hsu2,3, Zhi-Yong Liu2

  • 1Division of Cardiology, Chang Gung Memorial Hospital, Taoyuan, Taiwan.

Frontiers in Cardiovascular Medicine
|March 24, 2023
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Summary

Deep neural networks (DNNs) can use electrocardiograms (ECGs) to effectively screen for left ventricular systolic dysfunction (LVSD) and predict patient mortality. This AI-driven approach offers a low-cost, feasible method for cardiovascular risk assessment.

Keywords:
all-cause mortalitydeep neural networkelectrocardiogramleft ventricular ejection fractionleft ventricular systolic dysfunction

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Medical Imaging and Diagnostics

Background:

  • Left ventricular systolic dysfunction (LVSD), indicated by reduced left ventricular ejection fraction (LVEF), is linked to poor patient outcomes.
  • Standard 12-lead electrocardiogram (ECG) is a widely accessible diagnostic tool.
  • Developing AI models to leverage ECG for LVSD screening and prognosis is crucial.

Purpose of the Study:

  • To develop a deep neural network (DNN)-based model for screening LVSD using standard 12-lead ECG.
  • To stratify patient prognosis based on DNN-identified LVSD.
  • To evaluate the clinical feasibility and accuracy of ECG-based DNN models.

Main Methods:

  • Retrospective analysis of 190,359 adult patients with paired ECG and echocardiogram data.
  • Development of DNN models using original ECG signals or transformed images to detect LVSD (LVEF <40%).
  • External validation using data from 91,425 patients; mortality prediction assessed in over 1.19 million patients.

Main Results:

  • The signal-based DNN achieved an AUROC of 0.95 for LVSD detection with 0.91 sensitivity and 0.86 specificity.
  • DNN-predicted LVSD showed significant associations with all-cause (HR 2.57) and cardiovascular mortality (HR 6.09).
  • Positive DNN predictions in patients with preserved LVEF indicated increased risk for incident LVSD (HR 8.33).

Conclusions:

  • Deep neural networks enable ECG to serve as a low-cost, clinically practical tool for LVSD screening.
  • ECG-based DNN models provide accurate prognostication for patients with or at risk of LVSD.
  • This AI approach enhances the utility of ECG in cardiovascular disease management.