Arrhythmias

Multimodal Artificial Intelligence for Early Detection and Precision Management of Inflammatory and Infiltrative Cardiomyopathies.

BACKGROUND: Inflammatory and infiltrative cardiomyopathies, including cardiac sarcoidosis, transthyretin amyloidosis, and autoimmune myocarditis, are frequently underrecognized until advanced myocardial dysfunction develops. Conventional diagnosis of inflammatory and infiltrative cardiomyopathies depends on multimodality imaging and clinical integration, yet interpretation remains complex and dela...

EdgeECG: a lightweight edge-oriented network with dual criterion pruning for real-time ECG arrhythmia classification.

Objective.Miniature electrocardiogram (ECG) devices can rapidly and accurately acquire real-time cardiac signals, enabling timely warnings for patients with heart disease. To achieve accurate arrhythmia classification on resource-constrained ECG edge devices, we propose EdgeECG, an ultra-lightweight neural network designed for deployment on low-power microcontrollers.Approach.EdgeECG is first desi...

Cardiac activity monitoring for the heart rate classification with enhanced BiLSTM model.

Cardiac activity monitoring is important for assessing cardiovascular status and supporting computational analysis of heart-rate pattern variations fr...

Accelerated biological aging in bipolar disorder as determined by artificial intelligence-based electrocardiographic assessment.

BACKGROUND: Individuals with bipolar disorder (BD) are at increased risk for major adverse cardiovascular events. Recent evidence suggests that the di...

Artificial intelligence in the differential diagnosis of hypertrophic cardiomyopathy and physiological hypertrophy: a scoping review.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most common inherited myocardial disorder and a major cause of sudden cardiac death in young adul...

Deep learning electrocardiogram model for risk stratification of coronary revascularization need in the emergency department.

BACKGROUND AND AIMS: Identification of patients with acute coronary syndrome requiring coronary revascularization can be challenging due to inconclusi...

Signal-Based Assessment of Postural Stability Using ECG, EMG, and Center of Pressure Measures.

Postural stability reflects the integrated function of autonomic, neuromuscular, and postural control systems and deteriorates with aging and reduced ...

Generative AI for ECG Interpretation Education: Impact on Nursing, Student Performance, and AI Model Accuracy.

BACKGROUND: Electrocardiogram (ECG) interpretation is a critical yet challenging skill for nurses. Generative artificial intelligence (AI) offers pote...

Machine learning-guided risk stratification for Long QT Syndrome genetic variants with hiPSC-derived cardiomyocytes.

AIMS: Long QT syndrome (LQTS) is a life-threatening genetic disorder characterized by prolonged QT intervals on electrocardiograms. Congenital forms a...

Image Distortion Correction for Diffusion MR Imaging Using a Transformer-based U-Net.

PURPOSE: To evaluate the performance of a Transformer-based U-Net, TransDisCo, for distortion correction in clinical diffusion-weighted imaging (DWI),...

ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 Patients.

BACKGROUND: Scalable risk stratification for ischemic stroke remains an unmet need. OBJECTIVES: In this study, the authors sought to assess whether de...

Prediction of postoperative desaturation and bradycardia in neonates undergoing laparoscopic inguinal hernia repair: a retrospective cohort study.

BACKGROUND: Ex-premature infants have a high risk of postoperative apnea and bradycardia. This study aimed to develop a predictive model for postopera...

Diagnostic Performance Evaluation of Clinical and AI Risk Models in Patients Referred for Cardiac Amyloidosis Testing.

BACKGROUND: To improve screening for cardiac amyloidosis (CA), several models using artificial intelligence (AI) and conventional statistics have been...

A deep learning ECG model for identification and localization of occlusion myocardial infarction.

Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria m...

Transfer learning for Multi-institutional Classification of Intussusception and Splenomegaly in Pediatric Abdominal Radiographs.

To address diagnostic delays in pediatric abdominal emergencies, this study aimed to develop and validate multi-institutional deep learning models for...

Detecting cardiovascular diseases using ECG scans and explainable artificial intelligence.

BACKGROUND AND OBJECTIVE: Cardiovascular diseases are the leading cause of mortality globally, requiring early and accurate detection through tools li...

Machine learning-based prediction of diabetic retinopathy using clinlabomics: a multi-center study.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of vision loss, yet conventional retinal screening remains costly and resource-intensive. Thi...

Artificial Intelligence Versus Electrophysiologist Adjudication of Atrial Arrhythmias.

EJHF expert consensus statement on the diagnosis and management of hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is the most prevalent genetic cardiac disease and a leading cause of heart failure, arrhythmia, and sudden cardiac d...

Stacking Deep Neural Networks to Detect Multiple Types of Cardiac Arrhythmias.

Heart arrhythmias are associated with serious cardiovascular diseases and can result in fatal outcomes if not diagnosed early. Electrocardiograms (ECG...