Arrhythmias

Artificial intelligence and the evolution of the electrocardiogram: from cardiovascular diagnostic tool to digital biomarker.

The electrocardiogram (ECG) is a cornerstone of cardiovascular care. Traditionally, it has relied on expert visual interpretation and rule-based systems to define the presence of disease. However, the integration of artificial intelligence (AI) has transformed the ECG into a high-dimensional biomarker capable of detecting signatures of both overt and subclinical disease. This review explores the h...

Towards optimal valve prescription for transcatheter aortic valve replacement (TAVR) surgery: a machine learning approach.

Transcatheter Aortic Valve Replacement (TAVR) has emerged as a prominent, minimally invasive treatment for patients with severe aortic stenosis, a life-threatening cardiovascular condition. Multiple transcatheter heart valves (THV) have been approved for use in TAVR, but current guidelines regarding valve type prescription remain a topic of ongoing debate within the medical community. We propose a...

Differentiating Ischemic From Nonischemic T-Wave Inversion Using a Multimodal Vision-Language Model With Reinforcement Learning (ECG-R1): Development and Validation Study.

BACKGROUND: The differentiation of primary ischemic from secondary nonischemic T-wave inversion (TWI) on electrocardiograms (ECGs) presents a critical...

MSCA-TNet based deep learning method for ECG arrhythmia classification.

Electrocardiography (ECG) is a widely adopted modality for monitoring cardiac rate and rhythm and identifying various abnormalities of the cardiac ele...

Modeling day-long ECG signals to predict heart failure risk with explainable AI.

Heart failure (HF) affects 11.8% of adults aged 65 and older, reducing quality of life and longevity. Preventing HF can reduce morbidity and mortality...

Integrative multi-modal feature extraction from ECG signals using FusionHeartNet for robust heart disease prediction.

Electrocardiogram (ECG)-based diagnostics are pivotal in early cardiac disorder detection, yet existing models often fail to integrate temporal, spect...

The AI dispatcher copilot: beyond cardiac arrest recognition to dynamic large language model-assisted Tele-CPR.

AIM: Out-of-hospital cardiac arrest (OHCA) remains a leading cause of death. Although emergency medical dispatchers represent the first link in the Ch...

A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory.

Predicting the epidemic threshold [Formula: see text] in contact networks is a central challenge in computational epidemiology. Classical structural a...

Multitask Artificial Intelligence-Based Electrocardiogram Tool for Preoperative Cardiac Testing in Noncardiac Surgery: Retrospective Cohort Study of Health Care Utilization and Costs.

BACKGROUND: Preoperative cardiovascular risk stratification is essential in noncardiac surgery, but conventional testing is frequently overused, incre...

Artificial Intelligence-Enabled Serial Electrocardiograms for Prediction of All-Cause Mortality in Secondary Care Settings.

BACKGROUND: Prognostic assessment in secondary care settings remains challenging and may influence clinical decision-making and follow-up. Artificial ...

Integrative Identification of Frailty Status in Elderly Patients with Atrial Fibrillation Using a Noise-Resilient Multimodal Machine Learning Approach.

BACKGROUND: Frailty is common yet underdiagnosed in elderly patients with atrial fibrillation (AF), worsening outcomes and complicating treatment. Tra...

Persistent social connection over time is associated with attenuated biological ageing assessed by AI-enabled electrocardiography.

AIMS: Social isolation (SI) is associated with a higher risk of cardiovascular disease (CVD). One mechanism linking SI and CVD is accelerated biologic...

Artificial Intelligence for the Detection of Hypertrophic Cardiomyopathy From Standard Electrocardiogram.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often diagnosed late, increasing avoidable risk and delaying treatment. Artificial intelligence (AI) ...

Deep Learning-Based Prediction of Heart Failure Readmission Using Longitudinal Cardiac Device Data.

BACKGROUND: Heart failure (HF) is a leading cause of hospitalization and readmission. Cardiac implantable electronic devices (CIEDs) continuously capt...

Improving Trauma Triage Accuracy with Large Language Models: A Comparison to Human Expert Decisions.

BACKGROUND: Accurate prehospital trauma triage and communication determine morbidity, mortality, and system efficiency. Advancements in large language...

Explaining deep learning for ECG using time-localized clusters.

OBJECTIVE: Deep learning has advanced electrocardiogram (ECG) analysis but remains difficult to interpret, limiting clinical adoption and electrophysi...

Development and validation of a LightGBM-based model with risk stratification for predicting early recurrence after catheter ablation for atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is the most prevalent sustained arrhythmia, yet tools for predicting early recurrence (ER) after catheter ablatio...

AI-assisted diagnosis of cardiac amyloidosis using electrocardiograms and echocardiography: a multicenter retrospective study in China.

BACKGROUND: Cardiac amyloidosis (CA) is an under-recognized cause of left-ventricular hypertrophy (LVH) that is often misclassified as hypertrophic ca...

Artificial intelligence in the clinical management and prognostication of mitral regurgitation: a systematic review.

BACKGROUND: Mitral regurgitation (MR), one of the most common valvular heart diseases, poses ongoing challenges in risk stratification and timely inte...

Compact neural network algorithm for electrocardiogram classification.

In this paper, we present a powerful, compact electrocardiogram (ECG) classification algorithm for cardiac arrhythmia diagnosis that addresses the cur...