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Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.
Eur Heart J
January 2025
Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA.
Background And Aims: Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF risk.
Methods: Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), individuals without baseline HF were followed for the first HF hospitalization.
Background And Aims: The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain.
Methods: This was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain.
Quest for the ideal assessment of electrical ventricular dyssynchrony in cardiac resynchronization therapy.
J Mol Cell Cardiol Plus
March 2024
Department of Physiology and Cardiology, the Netherlands.
This paper reviews the literature on assessing electrical dyssynchrony for patient selection in cardiac resynchronization therapy (CRT). The guideline-recommended electrocardiographic (ECG) criteria for CRT are QRS duration and morphology, established through inclusion criteria in large CRT trials. However, both QRS duration and LBBB morphology have their shortcomings.
View Article and Find Full Text PDFDeep Learning for Obstructive Sleep Apnea Detection and Severity Assessment: A Multimodal Signals Fusion Multiscale Transformer Model.
Nat Sci Sleep
January 2025
Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Purpose: To develop a deep learning (DL) model for obstructive sleep apnea (OSA) detection and severity assessment and provide a new approach for convenient, economical, and accurate disease detection.
Methods: Considering medical reliability and acquisition simplicity, we used electrocardiogram (ECG) and oxygen saturation (SpO) signals to develop a multimodal signal fusion multiscale Transformer model for OSA detection and severity assessment. The proposed model comprises signal preprocessing, feature extraction, cross-modal interaction, and classification modules.
Enhanced detection of atrial fibrillation in single-lead electrocardiograms using a cloud-based artificial intelligence platform.
Heart Rhythm
January 2025
IDOVEN Research, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Myocardial Pathophysiology Area, Madrid, Spain; Centro de Investigación Biomédica en Red. Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain. Electronic address:
Background: Although smartphone-based devices have been developed to record 1-lead ECG, existing solutions for automatic atrial fibrillation (AF) detection often has poor positive predictive value.
Objective: This study aimed to validate a cloud-based deep learning platform for automatic AF detection in a large cohort of patients using 1-lead ECG records.
Methods: We analyzed 8,528 patients with 30-second ECG records from a single-lead handheld ECG device.
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