Arrhythmias

Classification models for KCNQ1 variants distinguish functional and trafficking effects to enhance pathogenicity interpretation.

Missense variants in the potassium channel KCNQ1 underlie most cases of congenital long QT syndrome (LQTS), one of the most common genetic arrhythmias. Variants affect protein stability, trafficking, and function, which are measurable properties that support variant interpretation. Leveraging the extensive experimental data generated by our laboratories, we developed random forest classifiers that...

Semantic edge detection of fractures in geological outcrops using edge aware dilated convolutional networks.

Natural fracture networks govern subsurface fluid flow, rock-mass stability, and strain accommodation in the brittle crust, yet their automated delineation from outcrop imagery remains challenging due to multi-scale size variability, low contrast between fracture boundaries and host-rock texture, and scene clutter from vegetation, shadows, and blast artifacts. Standard encoder-decoder networks app...

Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact.

Atrial fibrillation (AF) is the most prevalent sustained arrhythmia worldwide. Acute myocardial infarction (AMI) is closely intertwined with AF throug...

Machine learning algorithms for predicting arrhythmic events in Hypertrophic Cardiomyopathy: limited enhancement beyond late gadolinium enhancement.

We aimed to develop and assess the performance of a Machine learning (ML) model integrating common clinical features to predict arrhythmic events in p...

Morphology-preserving transfer-guided GAN with adaptive discriminative loss for robust and imbalanced ECG arrhythmia detection.

Electrocardiogram (ECG) based arrhythmia detection remains challenging due to severe class imbalance, morphological variability, and noise commonly pr...

A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients.

OBJECTIVE: Atrial fibrillation (AF) is the most common cardiac arrhythmia experienced by intensive care unit (ICU) patients and can cause adverse heal...

HPA-Net: a lightweight hierarchical progressive fusion network for cardiac disease detection based on ECG-PCG signals.

Cardiovascular disease (CVD) remains a leading global health threat, creating a strong clinical need for convenient and rapid diagnostic methods. Howe...

Design and Analysis of Emotion Elicitation Techniques for ECG-Based Emotion Recognition in Children with Autism Spectrum Disorder (ASD).

Children with autism spectrum disorder (ASD) face difficulties in expressing and recognizing emotions resulting in meltdowns and aggressive situations...

PanMB2-Net: a deep learning framework for preoperative MB2 canal risk assessment on panoramic radiographs.

OBJECTIVES: This study aimed to develop and validate a clinically motivated artificial intelligence framework for preoperative risk assessment of the ...

Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.

BACKGROUND: Pre-participation screening (PPS) in competitive athletes aims to identify cardiovascular diseases associated with sudden cardiac death (S...

A novel method for EKG anomaly detection based on the double sliding window technique.

Traditional detection methods often rely on fixed thresholding or machine-learning models, which can be computationally expensive. This study introduc...

Differentiating HFmr/rEF from HFpEF using standard 12-lead ECG measurements: an interpretable machine learning study.

BACKGROUND: Differentiating heart failure (HF) with mildly reduced/reduced ejection fraction (HFmr/rEF) from HF with preserved ejection fraction (HFpE...

A covalently adaptive polymer welding strategy enabling integrated electronics for pre-hospital monitoring of myocardial infarction.

Myocardial infarction (MI) is a major contributor to cardiovascular diseases (CVDs), creating an urgent demand for wireless, real-time, and continuous...

Acute Aluminum Phosphide Poisoning: Evolving Perspectives on Toxic Mechanisms and Evidence-Based Clinical Management.

Aluminum phosphide (AlP) is a chemical compound that is used as a pesticide for suicidal purposes and can cause death, and it poses a challenge to hea...

Comparative Interpretation of Pediatric Electrocardiograms by Large Language Models and a Pediatrician.

OBJECTIVE: Large language models (LLMs) have been explored for clinical applications, yet their reliability in pediatric electrocardiogram (ECG) inter...

Non-invasive arterial blood pressure waveform generation in critically ill patients: A sensor-based deep learning approach.

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of t...

Automated analysis of wearable ECG: machine learning methods, preprocessing pipelines, and benchmark datasets.

Wearable and mobile electrocardiography (ECG) has rapidly expanded access to rhythm monitoring outside of clinical settings, but the single-or few-lea...

Precision-Driven Management of Great Saphenous Varicose Veins: A Narrative Review.

OBJECTIVES: Great saphenous vein (GSV) incompetence is common, but numerous treatment options complicate patient-treatment matching. This narrative re...

A Dual-Modal Wearable PPG Smartwatch with AI-Enhanced Correction for High-Accuracy and Continuous AF Burden Assessment.

Atrial fibrillation (AF) increases the risk of stroke and heart failure, yet accurate quantification of AF burden in daily life remains difficult. Alt...

Multimodal Machine Learning Model Predicting Postoperative Delirium Based on Heart Rate Variability: A Prospective Observational Study.

BACKGROUND: Postoperative delirium is a common and serious complication after general anesthesia; its accurate prediction remains a substantial challe...