ArticleAnnals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc2021
Artificial intelligence for detecting electrolyte imbalance using electrocardiography.
Article in Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 61 citations in OpenAlex.
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- Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter study.Communications medicine · 2026Article
- 3D Printed Ion-Selective Electrodes Enriched with ZnO Nanoparticles for Potassium Detection.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Sports Cardiology: Advancing Cardiovascular Screening and Diagnosis.Cureus · 2026Review
- Enhanced prediction of left ventricular ejection fraction using electrocardiography with the addition of clinical metadata.The Korean journal of internal 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
- Abnormality prediction and forecasting of laboratory values from electrocardiogram signals using multimodal deep learning.Scientific reports · 2025Article
- Wearable Technology and Its Potential Role in Cardiovascular Health Monitoring and Disease Management.Health science reports · 2025Article
- Dynamic Prediction and Intervention of Serum Sodium in Patients with Stroke Based on Attention Mechanism Model.Journal of healthcare informatics research · 2025Article
- GenECG: a synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development.BMJ health & care informatics · 2025Article
- Point-of-care testing of hyponatremia and hypernatremia levels: An optoplasmonic biosensing approach.PloS one · 2025Article
- Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.JACC. Clinical electrophysiology · 2024Article
- Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings.Clinical journal of the American Society of Nephrology : CJASN · 2024Article
- Evaluating regression and probabilistic methods for ECG-based electrolyte prediction.Scientific reports · 2024Article
- Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety-A Narrative Review.Sports (Basel, Switzerland) · 2024Review
- Continual learning framework for a multicenter study with an application to electrocardiogram.BMC medical informatics and decision making · 2024Article
- Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians.Journal of Korean medical science · 2023Article
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. METHODS AND
resultsThis retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance.
conclusionThe proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.
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