Arrhythmias

AI-enhanced electrocardiography as a digital biomarker platform in cardiovascular medicine: clinical applications, validation gaps and future implementation pathways.

Artificial intelligence applied to the ECG is expanding the clinical role of this widely available diagnostic tool beyond conventional waveform interpretation by enabling identification of electrophysiological patterns associated with cardiovascular structure, function and risk. Within this evolving framework, the artificial intelligence enhanced ECG is emerging as a scalable digital biomarker pla...

Cardio-respiratory deep learning model to predict hospital admission from emergency department electrocardiogram images and respiration videos: a prospective study.

BACKGROUND: Early prediction of hospital admission at the emergency department (ED) triage can improve patient flow and resource allocation. Most existing models rely solely on structured data. Incorporating multimodal physiologic information, such as cardiac and respiratory signals, may better capture subtle clinical signs. This study aimed to develop a deep learning fusion model that integrates ...

Genotype-Specific Electrophysiological Remodeling in PLN R14del Cardiomyopathy: Implications for Precision Antiarrhythmic Therapy.

BACKGROUND: Inherited PLN (phospholamban) R14del variants cause dilated cardiomyopathy with a high burden of malignant ventricular arrhythmias. Howeve...

Prediction of incident atrial fibrillation from retinal fundus images using a multimodal foundation model.

Atrial fibrillation (AF), a common cardiac arrhythmia, presents significant challenges for early detection and management due to its asymptomatic and ...

An Intelligent IoT-Compatible Arrhythmia Detection System Using a Hybrid Vision Transformer-LSTM Framework.

BACKGROUND: In electrocardiogram (ECG) signal classification, advanced IoT-compatible systems and medical signal processing solutions have become feas...

Data-scarce transfer learning fusion for positron emission tomography and computed tomography guided lung biopsy.

OBJECTIVES: We developed a transfer learning-based multimodal fusion deep learning model integrating positron emission tomography/computed tomography ...

Point-of-care ultrasound in cardiovascular imaging: current status and future prospects.

INTRODUCTION: As ultrasound technology has become more advanced and accessible over the years, point-of-care ultrasound (POCUS) is becoming a tool as ...

Identifying the Presence and Characteristics of Mid-Myocardial and Epicardial Fibrosis From Intracardiac Electrograms in Patients Undergoing Ventricular Arrhythmia Ablation Using a Transformer-Based Self-Supervised Classifier.

BACKGROUND: Catheter ablation is an essential tool for ventricular arrhythmia management, yet sustained procedural success is hindered by the limited ...

Development of the CASPER-Score: A bedside tool integrating AI-based ECG analysis with key clinical predictors to identify relevant coronary artery stenosis in patients after out-of-hospital cardiac arrest.

INTRODUCTION: Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality worldwide, frequently associated with acute coronary syndromes. Wh...

Implementation of an ai-enabled multimodal emergency care system is associated with improved sudden cardiac death rescue outcomes in anyang.

Sudden Cardiac Death (SCD) remains a leading cause of mortality worldwide, with outcomes critically dependent on the effective implementation of the "...

A systematic evaluation of the reliability of feature selection methods and SHAP-based interpretability in machine learning models for arrhythmia analysis.

Arrhythmia is one of the most prevalent cardiovascular diseases worldwide. The classification of arrhythmias plays a major role in the diagnosis of he...

ECG arrhythmia classification via wavelet-driven feature extraction and swarm-optimised gradient boosting.

Cardiovascular diseases have been the primary contributor to deaths worldwide, and hence, the need to detect arrhythmia from Electrocardiogram signals...

Improving arrhythmic risk prediction using cardiac magnetic resonance within deep learning in ischemic heart disease.

Sudden arrhythmic death remains a major clinical risk in ischemic heart disease (IHD), underscoring the need for improved risk stratification. Late ga...

Precise ECG diagnosis and validation of educational utility for acute myocardial infarction using deep learning and explainable artificial intelligence.

Artificial intelligence (AI) holds significant promise for electrocardiogram (ECG) analysis, yet accurately detecting non-ST-segment elevation myocard...

Artificial intelligence-based prediction of transthoracic echocardiographic image quality in ICD/CRT-D candidates: A proof-of-concept study.

BACKGROUND: High-quality echocardiography is essential for accurate and reproducible assessment of cardiac functional indices, which are highly depend...

Preictal reduction in heart rate variability entropy is associated with functional/dissociative seizures and provides modest discrimination from epileptic seizures.

OBJECTIVE: Differentiating functional/dissociative seizures (FDS) from epileptic seizures (ES) remains clinically challenging, with limited electrocar...

ChemoCardioNet: An Explainable Multimodal Transformer for Early Prediction of Chemotherapy-Induced Cardiotoxicity.

Chemotherapy-induced cardiotoxicity (CIC) remains a major cause of morbidity and mortality among cancer survivors, and conventional monitoring often f...

Dynamic Beat-to-Beat Blood Pressure Estimation using a Multi-modal Wearable Deep Learning Approach.

Cuffless blood pressure (BP) monitoring technologies, primarily based on pulse transit time (PTT) or photoplethysmography (PPG), frequently suffer fro...

Endomyocardial Gremlin-1 is associated with structural remodeling and adverse clinical outcomes in non-ischemic cardiomyopathy.

BACKGROUND: Risk stratification in non-ischemic cardiomyopathies (NICM) remains challenging despite guideline-based phenotypic classification using mu...

Explainable ECG Analysis by Explicit Information Disentanglement With VAEs.

OBJECTIVE: The interpretation of electrocardiogram (ECG) signals is vital for diagnosis of cardiac conditions. Traditional methods rely on expert know...