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Evaluation of artificial intelligence-based electrocardiogram analysis tools in patients with hypertrophic

Gamze Babur Guler1, Arda Guler1, Ozgur Surgit1

  • 1Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Cardiology, Istanbul, Turkey.

European Heart Journal. Digital Health
|March 2, 2026
PubMed

Insights

Artificial intelligence (AI) tools for electrocardiogram (ECG) analysis showed limited accuracy in identifying hypertrophic cardiomyopathy (HCM) patients. Further validation is needed before widespread clinical use of these AI ECG tools in specialized cardiac populations.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Artificial intelligence (AI) tools for electrocardiogram (ECG) analysis show promise for cardiac condition detection.
  • The performance of these AI tools in specific patient groups, like hypertrophic cardiomyopathy (HCM), is not fully understood.

Purpose of the Study:

  • To evaluate the effectiveness of three AI-based ECG analysis tools in patients with confirmed HCM.
  • Assess AI tools for calculating HCM probability, structural heart disease (SHD) probability, and providing ECG-based diagnoses.

Main Methods:

  • Digitized 12-lead ECGs from 681 HCM patients were analyzed using three AI tools.
  • Assessed AI-calculated probability distributions, clinical parameter associations, and agreement with manual diagnoses using Cohen's kappa.

Main Results:

  • AI-calculated HCM probabilities had a median of 38.8%, with only 41.2% of patients scoring >50%.
  • HCM probabilities correlated with abnormal ECGs and disease severity markers.
  • SHD probabilities were higher, with a median of 51.4%, and 51.2% of patients scoring >50%.

Conclusions:

  • AI ECG analysis tools exhibited modest performance in the HCM cohort.
  • Challenges exist in applying general AI models to specific disease populations.
  • Disease-specific validation is crucial for AI tools before clinical implementation in cardiology.
Abstract

Keywords:
Artificial intelligenceDiagnostic accuracyElectrocardiogramHypertrophic cardiomyopathy

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