ArticleJMIR medical informatics2020
A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07493798 (Serum Potassium Prediction Using Machine Learning and Single-lead ECG), which is not on this map. Cited by 58 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.
Serum Potassium Prediction Using Machine Learning and Single-lead ECG
Who cites it
58 citing papers in PubMed, 103 citations in OpenAlex.
- AI-enabled electrocardiogram alert for potassium imbalance treatment: a pragmatic randomized controlled trial.Nature communications · 2026Trial
- Artificial Intelligence-Enabled ECGs for Atrial Fibrillation Identification and Enhanced Oral Anticoagulant Adoption: A Pragmatic Randomized Clinical Trial.Journal of the American Heart Association · 2025Trial
- Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease.Diagnostics (Basel, Switzerland) · 2026Review
- A Review of the Integration of Artificial Intelligence in Cardiac Electrophysiology.Journal of clinical medicine · 2026Review
- Cardiac parameter estimation from electrocardiograms based on inversion of physical simulator behavior.PLOS digital health · 2026Article
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- Review
- Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses.BioData mining · 2026Article
- Non-invasive hemoglobin estimation with outcome prediction via deep learning analysis of ECG-derived cardiac micro-dynamics.Internal and emergency medicine · 2026Article
- AI-enhanced non-invasive monitoring of critical biochemical analytes in perioperative care: a review of signal processing and clinical applications.Frontiers in physiology · 2026Review
- Artificial intelligence-enabled electrocardiography from scientific research to clinical application.EMBO molecular medicine · 2026Review
- Artificial Intelligence-Enabled Electrocardiography Identifies Osteoporosis and has Prognostic Value.Journal of medical systems · 2025Article
- ECG-based deep learning for chronic kidney disease detection and cardiovascular risk prediction.BMC medical informatics and decision making · 2025Article
- Electrocardiographic sex index: a continuous representation of sex.Biology of sex differences · 2025Article
- A deep learning phenome wide association study of the electrocardiogram.European heart journal. Digital health · 2025Article
- Development of an Artificial Intelligence-Enabled Electrocardiography to Detect 23 Cardiac Arrhythmias and Predict Cardiovascular Outcomes.Journal of medical systems · 2025Article
- AI analysis for ejection fraction estimation from 12-lead ECG.Scientific reports · 2025Article
- A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.Journal of imaging informatics in medicine · 2025Article
- Monitoring serum potassium concentration in patients with severe hyperkalemia: the role of bloodless artificial intelligence-enabled electrocardiography.Clinical kidney journal · 2025Article
- Non-Inferiority Analysis of Electrocardiography Analysis Application vs. Point-of-Care Ultrasound for Screening Left Ventricular Dysfunction.Yonsei medical journal · 2025Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe detection of dyskalemias-hypokalemia and hyperkalemia-currently depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia, electrocardiography (ECG) may be able to uncover clinically important dyskalemias before laboratory results.
objectiveOur study aimed to develop a deep-learning model, ECG12Net, to detect dyskalemias based on ECG presentations and to evaluate the logic and performance of this model.
methodsSpanning from May 2011 to December 2016, 66,321 ECG records with corresponding serum potassium (K
resultsIn a human-machine competition including 300 ECGs of different serum K+ concentrations, the area under the curve for detecting hypokalemia and hyperkalemia with ECG12Net was 0.926 and 0.958, respectively, which was significantly better than that of our best clinicians. Moreover, in detecting hypokalemia and hyperkalemia, the sensitivities were 96.7% and 83.3%, respectively, and the specificities were 93.3% and 97.8%, respectively. In a test set including 13,222 ECGs, ECG12Net had a similar performance in terms of sensitivity for severe hypokalemia (95.6%) and severe hyperkalemia (84.5%), with a mean absolute error of 0.531. The specificities for detecting hypokalemia and hyperkalemia were 81.6% and 96.0%, respectively.
conclusionsA deep-learning model based on a 12-lead ECG may help physicians promptly recognize severe dyskalemias and thereby potentially reduce cardiac events.
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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.