Myocardial Infarction

Artificial Intelligence-Enabled ECG for Elevated E/e' on Echocardiography: Hemodynamic Relevance and Prognostic Value.

BACKGROUND: Left ventricular filling pressure is associated with heart failure symptoms and a key prognostic marker and therapeutic target, but a scalable, accessible, and affordable tool for its noninvasive, serial estimation remains lacking. We developed an artificial intelligence (AI) model using a standard 12-lead ECG to detect increased E/e', a general surrogate of elevated left ventricular f...

Machine Learning-Based Risk Stratification for Symptomatic Intracranial Hemorrhage and 3-month Prognosis Following Intravenous Thrombolysis.

INTRODUCTION: Intravenous thrombolysis (IVT) with tissue-type plasminogen activator (tPA) is a cornerstone of acute ischemic stroke treatment, yet its benefits are limited by the risk of symptomatic intracranial hemorrhage (sICH), a complication associated with substantial morbidity and mortality. Functional recovery is commonly evaluated using the 3-month modified Rankin Scale (mRS). This study a...

MIMIC-III-Ext-PPG, a PPG-based Benchmark Dataset for Cardiovascular and Respiratory Signal Analysis.

We present MIMIC-III-Ext-PPG, a large-scale, quality-assessed photoplethysmography (PPG) dataset derived from the matched waveform subset of MIMIC-III...

External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.

An ECG-based artificial intelligence (AI) model was previously developed to generate ten digital biomarkers for emergency and cardiac assessment and i...

Predicting Acute Myocardial Infarction in the Absence of Detectable Atherosclerotic Plaque With Perivascular Coronary Inflammation.

BACKGROUND: Coronary computed tomography angiography (CTA) with analysis by artificial intelligence (AI) can personalize coronary artery disease risk ...

HeartBERT: A self-supervised ECG embedding model for efficient and effective medical signal analysis.

The analysis of Electrocardiogram (ECG) signals is critical for clinical applications, but current machine learning methods often face limitations whe...

A self-supervised electrocardiogram foundation model for empowering cardiovascular disease prediction and genetic factor discovery.

Electrocardiogram (ECG) has been widely used in the diagnosis of cardiovascular disease (CVD). Current deep learning methods for CVD prediction using ...

Artificial intelligence for detecting acute heart failure on chest CT: prospective clinical proof-of-concept validation.

OBJECTIVE: Acute heart failure (AHF) is a common but underrecognized cause of dyspnea. Chest computed tomography (CT) can accurately assess pulmonary ...

Prediction of Atrial Fibrillation Using Artificial Intelligence-Enhanced Electrocardiography　- Does Left Atrial Size Matter?

BACKGROUND: Artificial intelligence-enhanced electrocardiography (AI-ECG) for detecting atrial fibrillation (AF) using sinus rhythm ECGs has shown pro...

Prediction of Pregnancy-Related Cardiovascular Outcomes Using Electrocardiogram-Based Deep Learning Estimation of Cardiorespiratory Fitness.

BACKGROUND: Peak oxygen consumption (peak VO2), the gold standard measure of cardiorespiratory fitness, may identify women at high risk for pregnancy-...

Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction: Retrospective Algorithm Development and Validation Study.

BACKGROUND: Artificial intelligence-augmented electrocardiogram (AI-ECG) models for detecting left ventricular systolic dysfunction (LVSD) often exhib...

A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study.

BACKGROUND: Atrial fibrillation (AF) and atrial flutter (AFL) are common arrhythmias associated with the risk of ischemic stroke, which can be reduced...

Unsupervised Agglomerative Cluster Phenotyping of Young Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

BACKGROUND: Young patients with acute coronary syndrome (ACS) exhibit diverse demographic, clinical and angiographic characteristics. We hypothesized ...

Deep learning-based non-contrast cine CMR for optimized prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction.

OBJECTIVES: To evaluate the feasibility of a non-contrast cardiac magnetic resonance (CMR)-based deep learning (DL) model for predicting left ventricu...

Biobank-Scale Plasma Proteomics Identifies Novel Biomarkers in Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is characterized by substantial heterogeneity in both clinical phenotype and risk of adverse outcomes, i...

Nonperiodic dynamic CT reconstruction using backward-warping implicit neural representation with diffeomorphism regularization.

\textit{Objective.} Motion artifacts remain a major obstacle in dynamic computed tomography (CT) reconstruction, particularly for nonperiodic rapid mo...

Deep learning-based early prediction of carotid plaque response to lipid-lowering therapy using longitudinal multimodal ultrasound imaging.

OBJECTIVE: This study aimed to develop and validate a deep learning prediction model using longitudinal multimodal ultrasound imaging for early identi...

Low-latency HRV analysis from ultra-short ECG windows using a modular deep-learning framework.

We present a universal modular deep-learning framework and demonstrate its application to low-latency, streaming-compatible heart rate variability (HR...

Personalized Deep Networks for Enhanced ECG Segmentation.

Electrocardiography (ECG) plays a vital role in the diagnosis of cardiovascular diseases by analyzing the electrical activity of the heart. ECG semant...

From prediction to practice: an artificial neural network-derived risk score superior to conventional scales for predicting symptomatic intracranial haemorrhage risk after thrombolysis.