Myocardial Infarction

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like polysomnography are costly and impractical for long-term, home-based monitoring. This study presents an energy-efficient approach for detecting four sleep stages (wake, rapid eye movement (REM), light sleep, deep sleep) using a single-lead electrocardiogram...

Deep learning analysis of 12-lead electrocardiograms for bloodstream infection prediction: a multi-center validation study.

BACKGROUND: Prompt diagnosis of bloodstream infections (BSIs) is critical for antimicrobial stewardship but hindered by blood culture delays of 48 h or more. We developed and validated a deep learning model to predict BSI risk from standard 12-lead electrocardiograms (ECGs). METHODS: This study adhered to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Dia...

Artificial intelligence-enhanced nurse navigation for monitoring and care of long COVID.

INTRODUCTION: Long COVID is a multisystem condition with challenging diagnosis. Nurse-navigation, a patient-centered intervention, can enhance educati...

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...

The diagnostic and predictive value of AI-combined multilayer spiral CT for MACE after emergency PCI in STEMI patients: A prospective cohort study.

ST-segment elevation myocardial infarction (STEMI) patients remain at substantial risk for major adverse cardiovascular events (MACE) following emerge...

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...

Large Language Model Summarization of Physician-to-Physician Calls for Interhospital Transfer of Patients With ST-Elevation Myocardial Infarction: Observational Study.

BACKGROUND: Interhospital transfer of patients with suspected ST-elevation myocardial infarction (STEMI) requires timely and robust communication. Cli...

Serial OCT-based coronary physiology and plaque composition in vessels with nonobstructive coronary lesions following intensive lipid-lowering therapy: YELLOW III sub-study.

BACKGROUND AND AIMS: Non-obstructive coronary plaques are a major source of future coronary events, particularly when characterized by high lipid burd...

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 ...

Is cellular senescence a biological feature of Long COVID? A transcriptomic analysis across comparative post-acute sequelae phenotypes.

INTRODUCTION: Cellular senescence, involving cell-cycle arrest and inflammatory factor release, may play a role in Long COVID development. We investig...

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 ...

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...

Coronary computed tomography angiography to guide percutaneous coronary intervention: proceedings from the 1st CCTA-guided PCI summit in the United States.

Coronary computed tomography angiography (CCTA) can be used beyond diagnostic purposes to support the preprocedural planning of percutaneous coronary ...

Objective dyspnea measurement in real time using a forehead wearable and artificial intelligence.

Dyspnea is a complex symptom measured using subjective patient-reported ratings. Continuous, automated dyspnea measurements are needed, especially in ...

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...

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 ...

Super learner model for predicting carotid plaque regression in type 2 diabetes on PCSK9 inhibitors.

OBJECTIVES: Evidence regarding the effects of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on carotid plaque regression in patient...

Machine Learning Prediction of Heart Failure Readmissions: Insights From a Multicenter Emergency Department Trial.

BACKGROUND: Heart failure (HF) readmissions remain common and costly, yet existing prediction models show limited clinical utility, particularly at th...

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...