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September 18, 2025Heart & LungOpen Access

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

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Authors

PŁPaweł ŁajczakMedical University of SilesiaBRBruno Branco RighettoUniversidade PositivoOOOgechukwu ObiNew York Institute of Technology

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Overview

Systematic review demonstrates machine learning's potential in improving ECG diagnostic accuracy for hypertrophic cardiomyopathy.

Key Points

  • ML models achieved a pooled diagnostic accuracy of 0.959 for identifying hypertrophic cardiomyopathy.
  • The pooled area under the curve was 0.964, with sensitivity at 0.914 and specificity at 0.965.
  • Bivariate random-effects meta-analysis analyzed 21 studies for assessing diagnostic performance of ECG with ML.
  • Heterogeneity highlights the need for standardized ML approaches and further exploration of clinical applicability.

Cite This Study

Łajczak et al. (2025) studied this question.

synapsesocial.com/papers/68d461b631b076d99fa608a2https://doi.org/10.1016/j.hrtlng.2025.09.012
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