Myocardial Infarction

Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction With the Use of Electrocardiographic Artificial Intelligence: A Cost-Effectiveness Approach.

BACKGROUND: The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm for asymptomatic LVD detection and evaluate its cost-effectiveness for opportunistic ...

Deep learning-based dynamic ventilatory threshold estimation from electrocardiograms.

BACKGROUND AND OBJECTIVE: The ventilatory threshold (VT) marks the transition from aerobic to anaerobic metabolism and is used to assess cardiorespiratory endurance. A conventional way to assess VT is cardiopulmonary exercise testing, which requires a gas analyzer. Another method for measuring VT involves calculating the heart rate variability (HRV) from an electrocardiogram (ECG) by computing the...

Artificial intelligence-enhanced electrocardiogram for arrhythmogenic right ventricular cardiomyopathy detection.

AIMS: ECG abnormalities are often the first signs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and we hypothesized that an artificial int...

Impact of automatic acquisition of key clinical information on the accuracy of electrocardiogram interpretation: a cross-sectional study.

BACKGROUND: The accuracy of electrocardiogram (ECG) interpretation by doctors are affected by the available clinical information. However, having a co...

Does artificial intelligence enhance physician interpretation of optical coherence tomography: insights from eye tracking.

BACKGROUND AND OBJECTIVES: The adoption of optical coherence tomography (OCT) in percutaneous coronary intervention (PCI) is limited by need for real-...

Learning Skill Characteristics From Manipulations.

Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienc...

Comparison between optical coherence tomography-guided and intravascular ultrasound-guided primary percutaneous coronary intervention for ST-segment elevation myocardial infarction.

OBJECTIVE: To examine the clinical outcomes of optical coherence tomography (OCT)-guided percutaneous coronary intervention (PCI) in patients presenti...

Deep learning-based regional ECG diagnosis platform.

OBJECTIVE: To enable the intelligent diagnosis of a variety of common Electrocardiogram (ECG), we investigate the deep learning-based ECG diagnosis sy...

ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network.

Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In ...

International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction.

AIMS: A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) ...

Deep learning with fetal ECG recognition.

Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative val...

Cardiac Specific Troponin I as Prognostic Factor among Non-COVID-19 Mechanically Ventilated Patients in a Tertiary Government Hospital: A Prospective Study.

OBJECTIVE: The study aimed to investigate the relationship between cardiac Troponin I (cTnI) level and prognosis among mechanically ventilated patient...

Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community.

OBJECTIVES: The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovas...

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statist...

A deep learning analysis of stroke onset time prediction and comparison to DWI-FLAIR mismatch.

INTRODUCTION: When time since stroke onset is unknown, DWI-FLAIR mismatch rating is an established technique for patient stratification. A visible DWI...

Feasibility and limitations of deep learning-based coronary calcium scoring in PET-CT: a comparison with coronary calcium score CT.

OBJECTIVE: This study aimed to determine the feasibility and limitations of deep learning-based coronary calcium scoring using positron emission tomog...

Deep Learning Models for Predicting Left Heart Abnormalities From Single-Lead Electrocardiogram for the Development of Wearable Devices.

BACKGROUND: Left heart abnormalities are risk factors for heart failure. However, echocardiography is not always available. Electrocardiograms (ECGs),...

Artificial intelligence-enabled 8-lead ECG detection of atrial septal defect among adults: a novel diagnostic tool.

BACKGROUND: Patients with atrial septal defect (ASD) exhibit distinctive electrocardiogram (ECG) patterns. However, ASD cannot be diagnosed solely bas...

Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review.

BACKGROUND: The electrocardiogram (ECG) is one of the most common diagnostic tools available to assess cardio-vascular health. The advent of advanced ...

Safety and Effectiveness of an Anti-Xa-based Unfractionated Heparin Protocol for Impella Percutaneous Ventricular Assist Devices.

Impella devices are used for mechanical circulatory support in patients with cardiogenic shock or those undergoing high-risk percutaneous coronary in...