Arrhythmias

Patient-specific instantaneous spatial temperature maps for MR-guided laser interstitial thermal therapy using a physics-assisted deep learning framework.

PURPOSE: Accurate prediction of the laser energy absorption and corresponding thermal spread is essential for safe and effective outcomes in magnetic resonance-guided laser interstitial thermal therapy (MRgLITT), as it enables clinicians to anticipate thermal spread and ensure complete ablation of the epileptogenic focus while minimizing collateral damage. However, current planning tools rely on s...

Beat-to-beat variability of ventricular repolarization reveals sex-specific instability and proarrhythmic risk.

Beat-to-beat QT interval variability (QTV) is a well-established marker of increased vulnerability to ventricular arrhythmias; however the underlying electrophysiological mechanisms remain poorly understood. In this study, we employed sex-specific, physiologically detailed computational models of human ventricular myocytes to investigate the role of dynamical repolarization instability under basel...

Personalized artificial intelligence based left ventricular ejection fraction and systolic dysfunction assessment.

Left-ventricular (LV) ejection fraction (LVEF) is a fundamental measure of cardiac function, typically assessed with resource-intensive imaging techni...

Electrocardiogram-Based Mental Stress Detection Amid Everyday Activities Using Machine Learning: Model Development and Validation Study.

BACKGROUND: Frequent, sustained stress is linked to poor health and requires monitoring for early intervention. Electrocardiograms (ECG) are promising...

Interpretable machine learning models for stroke risk prediction in patients with newly diagnosed atrial fibrillation.

Atrial fibrillation (AF) is the most common sustained arrhythmia and a leading cause of ischemic stroke. Existing risk scores, such as CHA₂DS₂-VASc, o...

MAF-Net: Multimodal cross-attention-based fusion network for cardiovascular disease classification.

Cardiovascular disease ranks among the leading causes of death globally, posing a severe threat to human health. Consequently, rapid and accurate iden...

Deep Learning-based Segmentation for Assessment of Kidney Tumour Ablation Therapy in CT Images.

Kidney tumor ablation is a minimally invasive treatment for Renal Cell Carcinoma (RCC). Manual segmentation of the kidney ablation zone (KAZ) is time-...

Deep Learning Driven Evaluation of MR-guided Focused Ultrasound Ablation.

OBJECTIVE: Magnetic resonance-guided focused ultrasound (MRgFUS) thermal therapy is a promising incisionless procedure for breast cancer treatment. fo...

AI based ECG data recovery and cardiovascular diseases classification (CEDRC-network).

Cardiovascular diseases (CVDs) are a significant and widespread cause of death in the world, continuing to increase mortality rates. Therefore, timely...

Clinical implementation of 3D deep learning techniques in predicting touch-up lesions for atrial fibrillation patients undergoing cryoablation.

BACKGROUND: Atrial fibrillation (AF) is a common heart rhythm disorder that can be treated with cryoballoon ablation (CBA). CBA occasionally requires ...

Machine Learning Optimization of Laser Ablation in Liquid for the Green and Low-Cost Synthesis of Clean Gold Nanoparticles.

While gold nanoparticles (Au NPs) are widely employed in modern technology, their large-scale synthesis still faces challenges related to cost and sus...

Exploring the role of reinforcement learning in vision-language models for cardiovascular disease decision support.

OBJECTIVE: To explore the role of reinforcement learning (RL) in vision-language models (VLMs) for cardiovascular disease (CVD) decision support and a...

A lightweight and explainable cardiac signal framework for screening-oriented cardiometabolic risk assessment.

Early identification of cardiometabolic and autonomic dysfunction using electrocardiogram (ECG) signals is essential for preventive cardiovascular scr...

Serum Uric Acid-to-Creatinine Ratio and Atherogenic Index of Plasma as Independent Prognostic Indicators for Late Non-Valvular Atrial Fibrillation Recurrence After Catheter Ablation.

BACKGROUND: Atrial fibrillation (AF) is one of the most common clinical arrhythmias, and postoperative recurrence remains a major concern in cardiovas...

Deciphering atrial repolarization morphology: A spline interpolation framework for atrial arrhythmia diagnosis.

BackgroundThe characterization of atrial repolarization (Ta wave) remains largely elusive due to its inherently low amplitude and concealment beneath ...

Automated multi-class ECG arrhythmia detection using VMD and multi-task optimization.

Electrocardiogram (ECG) classification is essential for accurately detecting and tracking heart rhythm disorders. This study proposes a multi-class EC...

Re-evaluating heart rate variability biomarkers for glucose sensing: the impact of age normalisation and subject-independent validation.

BACKGROUND: Heart rate variability (HRV) derived from electrocardiogram (ECG) signals offers a promising non-invasive window into glycemic status; how...

Patient-Specific Cardio-Respiratory Model for Optimization of Cardiac Radioablation.

Stereotactic Arrhythmia Radioablation (STAR) is a promising treatment for refractory ventricular tachycardia. However, its precision may be hampered b...

Multiclass Arrhythmia Classification Using Multimodal Smartwatch Photoplethysmography Signals Collected in Real-Life Settings.

OBJECTIVE: Smartwatches with photoplethysmographic (PPG) sensors are ideal for early atrial fibrillation (AF) detection through continuous monitoring....

Feasibility of Free-breathing Deep Learning-reconstructed Single-Shot Cine MRI in Participants with Arrhythmia: Comparison with Conventional Segmented Cine MRI.

Purpose To evaluate the feasibility of retrospective electrocardiographically (ECG) gated single-shot cine using deep learning-enhanced compressed sen...