Arrhythmias

Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.

BACKGROUND: Ovarian cancer patients requiring intensive care unit (ICU) admission face particularly grave prognosis, yet current prognostic models rely on static baseline characteristics and generic severity scores, neglecting the rich temporal dynamics of vital signs that may better capture physiological deterioration patterns. OBJECTIVES: To develop and validate an interpretable Long Short-Term ...

From Chaos to Care: Personalized AI for Early Cardiac Arrhythmia Warning.

Cardiac arrhythmias are abnormal heart rhythms arising from disordered electrical dynamics that contribute significantly to global morbidity and mortality. Early prediction from physiological time series remains challenging due to nonlinear, nonstationary, and patient-specific cardiac dynamics. Although machine learning has advanced arrhythmia detection, most methods rely on static classification ...

An EMI-suppressed, high-fidelity Janus bioelectrode with gradient impedance for accurate dual-biosignal-based motion recognition.

Accurate acquisition of bioelectrical signals like electromyography (EMG) and electrocardiography (ECG) is essential for wearable health monitoring an...

Tumor ablation: emerging uses, challenges, and strategic implementation. A green paper by the Network of Expertise in Cancer (JANE-2), High Tech Medical Resources, network on Physical Methods of Tumor Ablation.

The field of oncology has witnessed remarkable progress with the integration of high-tech innovations in tumor ablation. Tumor ablation therapies, suc...

Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability.

Mental workload classification is critical in safety-sensitive fields such as healthcare and aviation. However, electroencephalography-based approache...

Rethinking prediction of sudden cardiac arrest: The role of electrocardiography in forecasting low-incidence, high-consequence events.

Sudden cardiac arrest (SCA) remains a leading cause of mortality, accounting for 300,000-400,000 deaths annually in the United States. Despite advance...

Serial AI-Enabled Electrocardiogram Trajectories During Heart Failure Treatment: A Proof-of-Concept Case Series.

BACKGROUND: Artificial intelligence-enabled electrocardiogram (AI-ECG) detects left ventricular systolic and diastolic dysfunction at single time poin...

Prediction of cardiac arrest in patients with heart failure in Sweden: a registry study with development of a machine learning model.

OBJECTIVE: 30-day survival after cardiac arrest is low, 12.4% and 36% for out-of-hospital and in-hospital cardiac arrest, respectively. Heart failure ...

Edge-AI enabled secure IoT framework for real-time patient monitoring and anomaly detection in smart healthcare systems.

Hospitals desire knowledge of bedside sensors in real time but they do not wish to send everything to the cloud. We propose an Edge-AI framework used ...

Advancing cardiac health assessment using deep convolutional neural networks for ECG image analysis.

This research introduces a novel technique for early prediction of cardiac affliction in ECG imagery. The initial phase involves pre-processing using ...

Cardiac substructure dosimetry in thoracic radiotherapy: moving beyond mean heart dose and toward precision cardiac risk mitigation.

BACKGROUND: Radiation-induced heart disease (RIHD) remains a clinically significant consequence of thoracic radiotherapy (RT). Historically, the mean ...

An ECG biomarker for sudden cardiac death discovered with deep learning.

Sudden cardiac death is, in theory, preventable with defibrillators. But every year, many patients die without defibrillators because doctors fail to ...

Efficacy and safety of multiple treatments for small hepatocellular carcinoma: an updated systematic review and component network meta analysis.

BACKGROUND: This is an updated component network meta-analysis to evaluate efficacy and safety of various therapeutic approaches and their combination...

Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.

BACKGROUND: Hypertension serves as a prevalent health issue, particularly in South Asia, where it is also a risk factor and comorbidity that affects t...

CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification.

Automated electrocardiogram (ECG) arrhythmia classification remains challenging due to morphological complexity, severe class imbalance, and poor mode...

Automatic measurement of vertebral compression ratio on lumbar MR images fracture assessment based on MS-Res-AttU-Net model framework.

OBJECTIVES: To develop an MS-Res-AttU-Net-based deep learning framework for automatic measurement of vertebral compression ratio (VCR) on lumbar magne...

Cardiometabolic multimorbidity and atrial fibrillation: insights from traditional statistical and artificial intelligence approaches.

BACKGROUND: Cardiometabolic multimorbidity (CMM) is increasingly prevalent among patients with atrial fibrillation (AF), yet its independent impact on...

Wearable devices for atrial fibrillation: diagnostic and screening roles of ECG and PPG-A systematic review.

OBJECTIVE: Atrial fibrillation (AF) is a major predictor of heart failure, stroke, and mortality. Traditional Holter monitors and event recorders are ...

Few-shot ECG analysis with large vision language models in data-scarce clinical settings.

Electrocardiogram (ECG) interpretation is fundamental for cardiac diagnosis. Machine learning has proven strong performance in ECG analysis but models...

Optimizing Amiodarone Maintenance Dose to Minimize N-Desethylamiodarone Accumulation and Pulmonary Toxicity: Insights from Machine Learning and Pharmacokinetic Simulation.

BACKGROUND AND OBJECTIVE: The accumulation of N-desethylamiodarone (DEA), an active metabolite of amiodarone, is a recognized risk factor for intersti...