Arrhythmias

Empowering atrial fibrillation detection with TabPFN and SHAP interpretation based on ECG-derived features: a dual-center temporal validation study.

Atrial fibrillation (AF) remains a leading driver of stroke and heart failure, yet timely diagnosis is frequently hindered by its asymptomatic nature and the limitations of current screening methods. This study aimed to develop and validate a highly accurate, interpretable, and scalable machine learning (ML) framework-TabPFN-for AF detection using standard 12-lead electrocardiogram (ECG)-derived f...

Assessment models for driving comfort and fatigue status based on heart rate variability.

OBJECTIVES: This study, from an interdisciplinary perspective of human factors engineering and biomedical engineering, aims to develop a real-time assessment model for driver status to mitigate the risks associated with fatigue and discomfort during prolonged driving. The primary objective is to construct a machine learning model based on Heart Rate Variability (HRV) that enables continuous and ob...

A teacher-student deep learning framework for enhanced clinical screening of heart failure from 12-lead electrocardiograms.

OBJECTIVE: Identifying heart failure (HF) from electrocardiograms (ECG) is challenging due to the lack of definitive features. This study aims to deve...

A dynamic light image enhancement algorithm using generative adversarial network for group activity recognition.

Video-based or image-based human activity recognition (HAR) via machine learning algorithms helps track, detect, and categorize users' daily activitie...

Exploratory association between multimodal AI-derived digital biomarkers and in-hospital mortality in adult patients with pneumonia: A proof-of-concept study.

Pneumonia remains a leading cause of in-hospital mortality worldwide. Current prognostic tools such as the IDSA/ATS severity score have meaningful lim...

Hybrid learning/numerical framework for fast and robust electric field simulation in irreversible electroporation.

OBJECTIVE: Irreversible electroporation (IRE) represents a promising non-thermal ablation modality for the treatment of deep-seated tumors. However, i...

The Rlign algorithm for enhanced electrocardiogram analysis through heart rate-corrected ECG alignment for explainable classification and clustering.

AIMS: Electrocardiogram (ECG) recordings are fundamental for diagnosing cardiac conditions. Recent advances in automatic ECG analysis have been domina...

AgroDualNet: a dual deep learning-based crop disease forecasting and fruit ripening detection.

Proper diagnosis of crop diseases and accurate measurement of fruit ripeness is essential in enhancing agricultural productivity, but conventional met...

Circulating miR-10b-5p as a Candidate Biomarker of Atrial Fibrillation Recurrence After Catheter Ablation: A Two-Phase Translational Study.

BACKGROUND: The identification of reliable biomarkers for atrial fibrillation (AF) recurrence post-catheter ablation remains a clinical challenge. Thi...

Artificial Intelligence-Enabled ECG for Elevated E/e' on Echocardiography: Hemodynamic Relevance and Prognostic Value.

BACKGROUND: Left ventricular filling pressure is associated with heart failure symptoms and a key prognostic marker and therapeutic target, but a scal...

External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.

An ECG-based artificial intelligence (AI) model was previously developed to generate ten digital biomarkers for emergency and cardiac assessment and i...

Integrating high-fidelity hiPSC-cardiomyocytes with AI-driven modeling for enhanced proarrhythmic risk assessment.

Cardiotoxicity remains the leading driver of drug attrition; however, its prediction remains suboptimal when conventional hERG assays and animal model...

Optimizing Cine Cardiac MRI: Technical Advances and Clinical Applications.

Cine cardiac magnetic resonance imaging (MRI) is the gold standard for cardiac function assessment, offering exceptional spatial and temporal resoluti...

Protocol to benchmark and evaluate the status of imbalance measure using correlation, data complexity, and ablation analyses.

Machine learning struggles with imbalanced data. Although several mitigation approaches exist, their application depends on the extent of imbalance. T...

HeartBERT: A self-supervised ECG embedding model for efficient and effective medical signal analysis.

The analysis of Electrocardiogram (ECG) signals is critical for clinical applications, but current machine learning methods often face limitations whe...

A self-supervised electrocardiogram foundation model for empowering cardiovascular disease prediction and genetic factor discovery.

Electrocardiogram (ECG) has been widely used in the diagnosis of cardiovascular disease (CVD). Current deep learning methods for CVD prediction using ...

Prediction of Atrial Fibrillation Using Artificial Intelligence-Enhanced Electrocardiography　- Does Left Atrial Size Matter?

BACKGROUND: Artificial intelligence-enhanced electrocardiography (AI-ECG) for detecting atrial fibrillation (AF) using sinus rhythm ECGs has shown pro...

Prediction of Pregnancy-Related Cardiovascular Outcomes Using Electrocardiogram-Based Deep Learning Estimation of Cardiorespiratory Fitness.

BACKGROUND: Peak oxygen consumption (peak VO2), the gold standard measure of cardiorespiratory fitness, may identify women at high risk for pregnancy-...

Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction: Retrospective Algorithm Development and Validation Study.

BACKGROUND: Artificial intelligence-augmented electrocardiogram (AI-ECG) models for detecting left ventricular systolic dysfunction (LVSD) often exhib...

A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study.

BACKGROUND: Atrial fibrillation (AF) and atrial flutter (AFL) are common arrhythmias associated with the risk of ischemic stroke, which can be reduced...