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Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.
Eur Heart J Acute Cardiovasc Care
January 2025
Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
Background: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep-learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.
Methods: In this retrospective cohort study, we analyzed the ECG data of 19,285 patients who visited ERs of three hospitals between 2016 and 2020; 9,119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study.
Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor.
Commun Med (Lond)
January 2025
Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
Background: The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.
Methods: We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated.
Development of rapid evaluation scale for pericardiectomy of tuberculous constrictive pericarditis by 12-lead electrocardiogram.
J Cardiothorac Surg
January 2025
Center for Translational Medicine, Huaihe Hospital, Henan University, Kaifeng, Henan, China.
Aim: We developed a rapid evaluation scale for pericardiectomy through a 12-lead electrocardiogram (ECG), in order to improve the diagnostic accuracy of pericardiectomy of tuberculous constrictive pericarditis.
Method: In this study, 262 patients with tuberculous constrictive pericarditis (102 patients) and non-tuberculous constrictive pericarditis (160 patients) were selected by convenience sampling method as participants in Hangzhou Red Corss Hospital from January 2018 to April 2023. The expert validity analysis was carried out by cross-sectional investigation combined with the results of the previous expert questionnaire to establish 12-lead ECG-based the rapid evaluate scale for pericardiectomy of tuberculous constrictive pericarditis.
The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.
JMIR Cardio
December 2024
School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
Background: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility.
View Article and Find Full Text PDFPoint-of-Care Potassium Measurement vs Artificial Intelligence-Enabled Electrocardiography for Hyperkalemia Detection.
Am J Crit Care
January 2025
Shih-Hua Lin is a professor, Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei.
Background: Hyperkalemia can be detected by point-of-care (POC) blood testing and by artificial intelligence- enabled electrocardiography (ECG). These 2 methods of detecting hyperkalemia have not been compared.
Objective: To determine the accuracy of POC and ECG potassium measurements for hyperkalemia detection in patients with critical illness.
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