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AI-driven ECG diagnostics: A game-changer for hypertrophic cardiomyopathy. A systematic review and diagnostic test

Paweł Łajczak1, Bruno Branco Righetto2, Ogechukwu Obi3

  • 1Department of Biophysics, Medical University of Silesia, Katowice, Poland.

Heart & Lung : the Journal of Critical Care
|September 18, 2025
PubMed

Insights

Machine learning (ML) models show high accuracy in diagnosing hypertrophic cardiomyopathy (HCM) using electrocardiogram (ECG) data. This approach offers a promising tool for early detection, especially in primary care settings.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Hypertrophic cardiomyopathy (HCM) is a prevalent inherited cardiovascular disease with potential for severe complications.
  • Accurate diagnosis is crucial for managing HCM and preventing adverse outcomes.
  • Electrocardiogram (ECG) combined with machine learning (ML) presents a novel diagnostic approach.

Purpose of the Study:

  • To systematically review and assess the diagnostic performance of ML algorithms utilizing ECG data for HCM detection.
  • To evaluate the accuracy, sensitivity, and specificity of ECG-ML models in identifying HCM.

Main Methods:

  • A systematic literature search was conducted across five major electronic databases.
  • Studies involving ML algorithms for HCM diagnosis using ECG data were included.
  • Bivariate random-effects meta-analysis was used to pool diagnostic metrics, with subgroup analyses to address heterogeneity.

Main Results:

  • Twenty-one studies were included in the meta-analysis.
  • The pooled area under the curve (AUC) was 0.964, with high pooled sensitivity (0.914) and specificity (0.965).
  • Overall diagnostic accuracy was 0.959, though significant heterogeneity (I² > 90%) and quality concerns were noted across studies.

Conclusions:

  • ML models demonstrate exceptional diagnostic accuracy for HCM detection via ECG.
  • These models hold potential as valuable tools in resource-limited settings and primary care.
  • Addressing heterogeneity, standardizing development/validation, and exploring explainable AI are crucial for clinical integration.
Abstract

Keywords:
CardiomyopathyDeep learningECGMachine learning

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