Arrhythmias

An ECG biomarker for sudden cardiac death discovered with deep learning.

Sudden cardiac death is, in theory, preventable with defibrillators. But every year, many patients die without defibrillators because doctors fail to predict their risk1. The only predictive biomarker in wide use, cardiac left ventricular ejection fraction (LVEF), misses most sudden cardiac deaths2, and flags many low-risk patients for futile defibrillators that never fire3,4. Here we apply deep l...

Efficacy and safety of multiple treatments for small hepatocellular carcinoma: an updated systematic review and component network meta analysis.

BACKGROUND: This is an updated component network meta-analysis to evaluate efficacy and safety of various therapeutic approaches and their combinations for small hepatocellular carcinoma (SHCC) following PRISMA 2020 guideline. METHODS: We screened two previously published meta-analyses on this topic, and searched PubMed, Embase, and the Cochrane Library from 1 January 2021 to 12 March 2026 for ran...

Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.

BACKGROUND: Hypertension serves as a prevalent health issue, particularly in South Asia, where it is also a risk factor and comorbidity that affects t...

CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification.

Automated electrocardiogram (ECG) arrhythmia classification remains challenging due to morphological complexity, severe class imbalance, and poor mode...

Automatic measurement of vertebral compression ratio on lumbar MR images fracture assessment based on MS-Res-AttU-Net model framework.

OBJECTIVES: To develop an MS-Res-AttU-Net-based deep learning framework for automatic measurement of vertebral compression ratio (VCR) on lumbar magne...

Cardiometabolic multimorbidity and atrial fibrillation: insights from traditional statistical and artificial intelligence approaches.

BACKGROUND: Cardiometabolic multimorbidity (CMM) is increasingly prevalent among patients with atrial fibrillation (AF), yet its independent impact on...

Wearable devices for atrial fibrillation: diagnostic and screening roles of ECG and PPG-A systematic review.

OBJECTIVE: Atrial fibrillation (AF) is a major predictor of heart failure, stroke, and mortality. Traditional Holter monitors and event recorders are ...

Few-shot ECG analysis with large vision language models in data-scarce clinical settings.

Electrocardiogram (ECG) interpretation is fundamental for cardiac diagnosis. Machine learning has proven strong performance in ECG analysis but models...

Optimizing Amiodarone Maintenance Dose to Minimize N-Desethylamiodarone Accumulation and Pulmonary Toxicity: Insights from Machine Learning and Pharmacokinetic Simulation.

BACKGROUND AND OBJECTIVE: The accumulation of N-desethylamiodarone (DEA), an active metabolite of amiodarone, is a recognized risk factor for intersti...

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 lif...

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...