AI-driven ECG diagnostics: A game-changer for hypertrophic cardiomyopathy. A systematic review and diagnostic test accuracy meta-analysis
Łajczak P, Righetto BB, Obi O, Wijaya P, Sahin OK, Eltawansy S, Salha I, Łajczak A, Schincariol M (2026)
Publication Type: Journal article, Review article
Publication year: 2026
Journal
Book Volume: 75
Pages Range: 71-82
DOI: 10.1016/j.hrtlng.2025.09.012
Abstract
Background: Hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disorder that may cause serious complications. Accurate diagnosis of HCM is essential to mitigate adverse outcomes. The electrocardiogram (ECG), combined with advancements in machine learning (ML), presents a promising alternative for HCM diagnosis and this systematic review aimed to assess ECG-ML performance. Objectives: This study aims to evaluate diagnostic accuracy of ECG-ML. Methods: A search was conducted across five major electronic databases. Studies were included if they assessed ML algorithms for diagnosing HCM using ECG data. Bivariate random-effects meta-analysis was employed to pool diagnostic metrics, and subgroup analyses were performed to explore heterogeneity. Results: A total of 21 studies were included. The pooled area under curve was 0.964 (95% CI 0.906–0.979) Sensitivity and specificity were 0.914 (95% CI 0.847–0.953) and 0.965 (95% CI 0.889–0.989), respectively. The diagnostic odds ratio (DOR) was 250.796, and the overall accuracy was 0.959 (95% CI 0.893–0.985). Heterogeneity was observed (I² > 90%). Subgroup analyses indicated variations in diagnostic performance based on ML model type, validation methods, and geographic origin. Quality concerns were found. Conclusion: ML models demonstrated exceptional diagnostic accuracy in identifying HCM from ECG data, showing potential as effective diagnostic tools in resource-limited settings and primary care. However, heterogeneity and quality concerns highlight the need for standardized ML development and validation. Future research should focus on addressing these limitations, exploring explainable AI methods, and conducting to ensure clinical applicability.
Authors with CRIS profile
Involved external institutions
Medical University of Silesia in Katowice / Śląski Uniwersytet Medyczny w Katowicach (SUM)
Poland (PL)
Universidade Positivo (UP)
Brazil (BR)
New York Institute of Technology
United States (USA) (US)
University of Indonesia (UI) / Universitas Indonesia
Indonesia (ID)
Edremit State Hospital / Edremit Devlet Hastanesi
Turkey (TR)
Jersey Shore University Medical Center (JSUMC)
United States (USA) (US)
Trinity College Dublin
Ireland (IE)
Poland (PL)
Universidade Positivo (UP)
Brazil (BR)
New York Institute of Technology
United States (USA) (US)
University of Indonesia (UI) / Universitas Indonesia
Indonesia (ID)
Edremit State Hospital / Edremit Devlet Hastanesi
Turkey (TR)
Jersey Shore University Medical Center (JSUMC)
United States (USA) (US)
Trinity College Dublin
Ireland (IE)
How to cite
APA:
Łajczak, P., Righetto, B.B., Obi, O., Wijaya, P., Sahin, O.K., Eltawansy, S.,... Schincariol, M. (2026). AI-driven ECG diagnostics: A game-changer for hypertrophic cardiomyopathy. A systematic review and diagnostic test accuracy meta-analysis. Heart & Lung, 75, 71-82. https://doi.org/10.1016/j.hrtlng.2025.09.012
MLA:
Łajczak, Paweł, et al. "AI-driven ECG diagnostics: A game-changer for hypertrophic cardiomyopathy. A systematic review and diagnostic test accuracy meta-analysis." Heart & Lung 75 (2026): 71-82.
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