ReviewJournal of the Saudi Heart Association2025
Artificial Intelligence-enhanced Electrocardiography for Hypertrophic Cardiomyopathy Diagnosis: A Systematic Review and Meta-analysis.
Review in Journal of the Saudi Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Evaluation of artificial intelligence-based electrocardiogram analysis tools in patients with hypertrophic cardiomyopathy.European heart journal. Digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Diagnosing hypertrophic cardiomyopathy (HCM) can be challenging due to its nonspecific clinical manifestations, variability in electrocardiographic (ECG) patterns, and limited access to echocardiography, the gold standard for diagnosis, often leading to delayed detection. Recent artificial intelligence (AI) advancements have enabled ECG-based algorithms to improve HCM detection. This systematic review and meta-analysis aim to assess the overall diagnostic performance of AI-enhanced ECG in identifying HCM. Methods: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Articles were retrieved from PubMed, EBSCO, and Proquest. Inclusion criteria encompassed all studies evaluating AI algorithms for the detection of HCM from 12-lead ECGs. Meta-analysis was performed using R v4.4.1. Bivariate random-effects models were employed to derive pooled estimates of sensitivity, specificity, and the area under the curve (AUC) of the summary receiver operating characteristic (SROC). Results: A total of five retrospective cohort studies involving 69,343 participants, were included. The pooled sensitivity of AI-enhanced ECG for detecting HCM was 0.84, and the specificity was 0.86. The AI-enhanced ECG demonstrated excellent diagnostic accuracy, with an SROC-AUC of 0.927 in detecting HCM. Conclusion: AI-enhanced ECG shows promise as a novel screening tool for detecting hypertrophic cardiomyopathy. However, the considerable heterogeneity and the limited number of studies necessitate careful interpretation and highlight the need for additional research in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.