ArticleJournal of clinical hypertension (Greenwich, Conn.)2021
Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach.
Article in Journal of clinical hypertension (Greenwich, Conn.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Automated estimation of computed tomography-derived left ventricular mass using sex-specific 12-lead ECG-based temporal convolutional network.European heart journal. Digital health · 2026Article
- Enhancing cardiac disease prediction with explainable bidirectional LSTM.Scientific reports · 2025Article
- Comparison of machine learning and conventional criteria in detecting left ventricular hypertrophy and prognosis with electrocardiography.European heart journal. Digital health · 2025Article
- Article
- Multi-class: spectral-spatial temporal pyramid network and multi-class classifier-based cardiovascular disease classification.Frontiers in physiology · 2025Article
- Based on Bayesian multivariate skewed regression analysis: the interaction between skeletal muscle mass and left ventricular mass.Frontiers in physiology · 2025Article
- An ECG-Based Model for Left Ventricular Hypertrophy Detection: A Machine Learning Approach.IEEE open journal of engineering in medicine and biology · 2025Article
- Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Prognostic implications of heart failure stages among Chinese community populations: insight from a nationwide population-based study.The Lancet regional health. Western Pacific · 2024Article
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning.European heart journal. Digital health · 2023Article
- Are Electrocardiographic Criteria Reliable for Left Ventricular Hypertrophy Detection in Indian Adults?Cureus · 2023Article
- Survey and Evaluation of Hypertension Machine Learning Research.Journal of the American Heart Association · 2023Article
- Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Explainable machine learning predictions to support personalized cardiology strategies.European heart journal. Digital health · 2022Article
- Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry.Archivos de cardiologia de Mexico · 2022Article
- Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach.Journal of clinical hypertension (Greenwich, Conn.) · 2021Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cardiac remodeling is recognized as an important aspect of cardiovascular disease (CVD) progression. Machine learning (ML) techniques were applied to basic clinical parameters and electrocardiographic features, in order to detect abnormal left ventricular geometry (LVG) even before the onset of left ventricular hypertrophy (LVH), in a population without established CVD. The authors enrolled 528 patients with and without essential hypertension, but no other indications of CVD. All patients underwent a full echocardiographic evaluation and were classified into 3 groups; normal geometry (NG), concentric remodeling without LVH (CR), and LVH. Abnormal LVG was identified as increased relative wall thickness (RWT) and/or left ventricular mass index (LVMi). The authors trained supervised ML models to classify patients with abnormal LVG and calculated SHAP values to perform feature importance and interaction analysis. Hypertension, age, body mass index over the Sokolow-Lyon voltage, QRS-T angle, and QTc duration were some of the most important features. Our model was able to distinguish NG from CR+LVH combined, with 87% accuracy on an unseen test set, 75% specificity, 97% sensitivity, and area under the receiver operating curve (AUC/ROC) equal to 0.91. The authors also trained our model to classify NG and CR (NG + CR) against those with LVH, with 89% test set accuracy, 93% specificity, 67% sensitivity, and an AUC/ROC value of 0.89, for a 0.4 decision threshold. Our ML algorithm effectively detects abnormal LVG even at early stages. Innovative solutions are needed to improve risk stratification of patients without established CVD, and ML may enable progress in this direction.
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