Arrhythmias

Procedural Strategies for Optimal Transcatheter Aortic Valve Replacement: An International Position Statement.

The extension of transcatheter aortic valve replacement (TAVR) to younger patients with longer life expectancy has driven a shift in focus toward procedural optimization, with the goals of maximal clinical improvement, durable outcomes, maintained coronary access, and avoidance of permanent pacemaker implantation. A TAVR CODE framework including 4 key fluoroscopic parameters-coaxiality, orientatio...

Impact of an AI algorithm for multi-day prediction of incident atrial fibrillation on clinical decision-making: PROVISION-AF study.

Atrial fibrillation (AF) is frequently asymptomatic and often remains undetected until complications arise. Although artificial intelligence (AI)-enabled electrocardiography (ECG) can predict incident AF from sinus rhythm ECGs, its influence on physician risk assessment in simulated clinical settings remains uncertain. We developed a deep learning model to predict multi-day AF risk using non-AF 12...

Artificial intelligence in cardiopulmonary resuscitation: a systematic review.

Cardiac arrest remains a major cause of mortality and neurological disability, and its management depends on rapid recognition, effective resuscitatio...

Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease.

Portable, scalable, and accessible artificial intelligence (AI)-enabled smartwatch technology shows promise as a cardiovascular risk stratification st...

A robust binary secretary bird optimization method for high-dimensional data classification.

Feature selection is a key step in machine learning-based decision systems, especially in medical and biomedical applications, where datasets often co...

Cerebellum-inspired memtransistors enable emergent differentiation for hardware-efficient novelty detection.

Artificial intelligence (AI) algorithms are currently executed using silicon-based hardware, resulting in excessively high energy demand for data cent...

Prediction model for recurrent laryngeal nerve injury in microwave ablation of thyroid nodules.

OBJECTIVE: This study aims to develop an explainable machine learning (ML) framework integrating clinical, imaging, and procedural features for predic...

A three-level risk stratification algorithm for arrhythmia based on CNN-LSTM palindromic structure.

Cardiovascular diseases are characterized by sudden onset, high mortality rates, and high recurrence rates, making early screening and timely interven...

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 interp...

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 exis...

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...