SynthesisScientific reports2024
Diagnostic accuracy of artificial intelligence in detecting left ventricular hypertrophy by electrocardiograph: a systematic review and meta-analysis.
Synthesis in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Fabry disease cardiomyopathy: A state-of-the-art review.Progress in cardiovascular diseasesPooled it
- Machine learning to classify left ventricular hypertrophy using electrocardiographic feature extraction by variational autoencoder.Heart rhythm O2 · 2026Article
- Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses.BioData mining · 2026Article
- Deep learning to predict left ventricular hypertrophy from the electrocardiogram.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
- Artificial Intelligence in Sports Cardiology: Advancing Cardiovascular Screening and Diagnosis.Cureus · 2026Review
- Precision medicine and personalized nursing in cardiovascular disease: clinical applications and frontier developments.Frontiers in cardiovascular medicine · 2026Review
- MiR-107 as a Biomarker Predicts Cardiac Hypertrophy in Chronic Hemodialysis Patients.Anatolian journal of cardiology · 2025Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Machine learning to classify left ventricular hypertrophy using ECG feature extraction by variational autoencoder.medRxiv : the preprint server for health sciences · 2025Article
- Developing a risk score for B-type natriuretic peptide (BNP) ≥ 100 pg/ml levels: the suita study.BMC cardiovascular disorders · 2025Article
- Review
- Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram-Applicable in Clinical Practice?-Critical Literature Review with Meta-Analysis.Healthcare (Basel, Switzerland) · 2025Review
- Progress in diagnosis and treatment of hypertension combined with left ventricular hypertrophy.Annals of medicine · 2024Review
- Artificial intelligence in modern clinical practice (Review).Medicine internationalReview
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several studies suggested the utility of artificial intelligence (AI) in screening left ventricular hypertrophy (LVH). We hence conducted systematic review and meta-analysis comparing diagnostic accuracy of AI to Sokolow-Lyon's and Cornell's criteria. Our aim was to provide a comprehensive overview of the newly developed AI tools for diagnosing LVH. We searched MEDLINE, EMBASE, and Cochrane databases for relevant studies until May 2023. Included were observational studies evaluating AI's accuracy in LVH detection. The area under the receiver operating characteristic curves (ROC) and pooled sensitivities and specificities assessed AI's performance against standard criteria. A total of 66,479 participants, with and without LVH, were included. Use of AI was associated with improved diagnostic accuracy with summary ROC (SROC) of 0.87. Sokolow-Lyon's and Cornell's criteria had lower accuracy (0.68 and 0.60). AI had sensitivity and specificity of 69% and 87%. In comparison, Sokolow-Lyon's specificity was 92% with a sensitivity of 25%, while Cornell's specificity was 94% with a sensitivity of 19%. This indicating its superior diagnostic accuracy of AI based algorithm in LVH detection. Our study demonstrates that AI-based methods for diagnosing LVH exhibit higher diagnostic accuracy compared to conventional criteria, with notable increases in sensitivity. These findings contribute to the validation of AI as a promising tool for LVH detection.
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Registered trials
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