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Artificial Intelligence-Enhanced Electrocardiogram Models for Detection of Left Ventricular Dysfunction: A Comparison

Philip M Croon1, Machteld J Boonstra2, Cornelis P Allaart3

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Summary
This summary is machine-generated.

Artificial intelligence-enhanced electrocardiogram (AI-ECG) models show strong performance in detecting left ventricular systolic dysfunction (LVSD). However, limited model availability hinders independent validation and comparison of AI-ECG tools for LVSD detection.

Keywords:
artificial intelligencedeep learningelectrocardiographyheart failureleft ventricular dysfunction

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Several artificial intelligence-enhanced electrocardiogram (AI-ECG) models show promise for detecting left ventricular systolic dysfunction (LVSD).
  • Independent head-to-head comparisons and performance evaluations within a single cohort are lacking.
  • Existing models often suffer from high risk of bias due to limited cohort descriptions and lack of external validation.

Purpose of the Study:

  • To independently compare the performance of published AI-ECG models for LVSD detection.
  • To evaluate the transparency and reproducibility of AI-ECG models in cardiology.
  • To assess AI-ECG model performance in a standardized external cohort.

Main Methods:

  • Systematic review of AI-ECG models for LVSD prediction.
  • External validation of shared AI-ECG models in a well-phenotyped cardiac magnetic resonance imaging registry.
  • Performance evaluation in the overall patient cohort and a lower-complexity subgroup.

Main Results:

  • Identified 51 AI-ECG models from 35 studies, with many reporting high performance (AUROC >0.80).
  • Independent validation showed area under the receiver-operating characteristic curve (AUROC) ranging from 0.83 to 0.93 in all patients and 0.87 to 0.96 in a lower-complexity subset.
  • Model performance remained consistent across subgroups, with minor decreases observed in ECGs with wide QRS complexes or atrial fibrillation.

Conclusions:

  • AI-ECG models demonstrate strong performance for LVSD detection, even when trained on diverse populations.
  • This study represents the first independent validation and head-to-head comparison of AI-ECG models for LVSD.
  • Limited availability of AI-ECG models impedes comprehensive independent validation and clinical adoption.