ReviewCirculation reports2019
Application of Neural Networks to 12-Lead Electrocardiography - Current Status and Future Directions.
Review in Circulation reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 21 citations in OpenAlex.
- The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Myocardial Infarction Detection via Electrocardiogram: A Scoping Review.Journal of clinical medicine · 2025Review
- GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19.Journal of imaging · 2025Article
- Review
- Integration of Artificial Intelligence in Medicines.JMA journal · 2024Article
- Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms.Nature communications · 2024Article
- The Importance of External Validation for Neural Network Models.JACC. Advances · 2023Article
- Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study.EClinicalMedicine · 2023Article
- Detection of the Relationship between the Multi-Dimensional Data Sets of Serially Measured Blood Pressure and the Future Risk of Death in Healthy Elderly Japanese Population.Journal of atherosclerosis and thrombosis · 2023Article
- An artificial intelligence-enabled ECG algorithm for identifying ventricular premature contraction during sinus rhythm.European journal of medical research · 2022Article
- Development of Novel Artificial Intelligence to Detect the Presence of Clinically Meaningful Coronary Atherosclerotic Stenosis in Major Branch from Coronary Angiography Video.Journal of atherosclerosis and thrombosis · 2021Article
- A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The 12-lead electrocardiogram (ECG) is a fast, non-invasive, powerful tool to diagnose or to evaluate the risk of various cardiac diseases. The vast majority of arrhythmias are diagnosed solely on 12-lead ECG. Initial detection of myocardial ischemia such as myocardial infarction (MI), acute coronary syndrome (ACS) and effort angina is also dependent upon 12-lead ECG. ECG reflects the electrophysiological state of the heart through body mass, and thus contains important information on the electricity-dependent function of the human heart. Indeed, 12-lead ECG data are complex. Therefore, the clinical interpretation of 12-lead ECG requires intense training, but still is prone to interobserver variability. Even with rich clinically relevant data, non-trained physicians cannot efficiently use this powerful tool. Furthermore, recent studies have shown that 12-lead ECG may contain information that is not recognized even by well-trained experts but which can be extracted by computer. Artificial intelligence (AI) based on neural networks (NN) has emerged as a strong tool to extract valuable information from ECG for clinical decision making. This article reviews the current status of the application of NN-based AI to the interpretation of 12-lead ECG and also discusses the current problems and future directions.
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.