ArticleNPJ digital medicine2025
Identification of cardiac wall motion abnormalities in diverse populations by deep learning of the electrocardiogram.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Identifying the Presence and Characteristics of Mid-Myocardial and Epicardial Fibrosis From Intracardiac Electrograms in Patients Undergoing Ventricular Arrhythmia Ablation Using a Transformer-Based Self-Supervised Classifier.Circulation. Arrhythmia and electrophysiology · 2026Article
- An efficient deep CNN based BiLSTM framework with RanA optimization for accurate cardiac arrhythmia detection.Scientific reports · 2026Article
- Deep Learning-Based Continuous QT Monitoring to Identify High-Risk Prolongation Events After Class III Antiarrhythmic Initiation.Circulation · 2026Article
- Long-term, ambulatory 12-lead ECG from a single non-standard lead using perceptual reconstruction.medRxiv : the preprint server for health sciences · 2025Article
- Advances in Electrocardiogram-Based Artificial Intelligence Reveal Multisystem Biomarkers.Journal of clinical & experimental cardiology · 2025Article
- Burden of ageing spectrum of diseases in China, 1990-2021: a systematic analysis of global burden of disease study 2021.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports. A deep neural network (ECG-WMA-Net) was trained and outperformed both expert ECG interpretation and Q-wave indices, achieving an AUROC of 0.781 (CI: 0.762-0.799). The model was externally validated in a diverse cohort from Georgia (n = 2338), with an AUC of 0.723 (CI: 0.685-0.757). Explainability analysis revealed significant contributions from QRS and T-wave regions. This deep learning approach improves WMA screening accuracy, potentially addressing physiological differences not captured by standard ECG-based methods.
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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.