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Artificial Intelligence-enhanced Electrocardiography for Hypertrophic Cardiomyopathy Diagnosis: A Systematic Review

Fernando A Theja1, Louis F J Jusni1, Robby Soetedjo1

  • 1Faculty of Medicine, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia.

Journal of the Saudi Heart Association
|July 1, 2025
PubMed

Insights

Artificial intelligence (AI) algorithms applied to electrocardiograms (ECGs) show promise for detecting hypertrophic cardiomyopathy (HCM). This AI-enhanced ECG approach achieved high accuracy in a meta-analysis, suggesting its potential as a novel screening tool.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Diagnosing hypertrophic cardiomyopathy (HCM) is challenging due to nonspecific symptoms and variable ECG patterns.
  • Limited access to echocardiography can delay HCM detection.
  • Artificial intelligence (AI) offers potential for improved ECG-based diagnostics.

Purpose of the Study:

  • To systematically review and meta-analyze the diagnostic performance of AI-enhanced ECG for HCM detection.
  • To assess the sensitivity, specificity, and accuracy of AI algorithms in identifying HCM from ECG data.

Main Methods:

  • Systematic review and meta-analysis adhering to PRISMA guidelines.
  • Inclusion of retrospective cohort studies evaluating AI for 12-lead ECG-based HCM detection.
  • Bivariate random-effects models used to calculate pooled sensitivity, specificity, and SROC-AUC.

Main Results:

  • Five studies with 69,343 participants were included.
  • AI-enhanced ECG demonstrated a pooled sensitivity of 0.84 and specificity of 0.86 for HCM detection.
  • The summary receiver operating characteristic area under the curve (SROC-AUC) was 0.927, indicating excellent diagnostic accuracy.

Conclusions:

  • AI-enhanced ECG shows significant potential as a novel screening tool for hypertrophic cardiomyopathy.
  • Further research is needed due to study heterogeneity and a limited number of included studies.
Abstract

Keywords:
AI-enhanced ECGDiagnosticHypertrophic cardiomyopathy

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