Myocardial Infarction

[Advances in chest imaging in early chronic obstructive pulmonary disease].

Chronic obstructive pulmonary disease(COPD)is a heterogeneous and complex disease, and is characteri...

Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study.

Given the psychosocial and ethical burden, patients with hypertrophic cardiomyopathy (HCMs) could b...

Changes of plasma Rap1A levels in patients with in-stent restenosis after percutaneous coronary intervention and the underlying mechanisms.

OBJECTIVES: Percutaneous coronary intervention (PCI) is one of the most important treatments for cor...

Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography.

UNLABELLED: Background CT is helpful in guiding the revascularization of chronic total occlusion (CT...

Twenty-six years of machine learning for ECG: and we are not there yet.

[Saved myocardium in acute ST-segment elevation myocardial infarction post-reperfusion: Analysis by cardiac magnetic resonance].

OBJECTIVE: To quantify by cardiovascular magnetic resonance the salvaged myocardium in the myocardiu...

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG.

QT prolongation often leads to fatal arrhythmia and sudden cardiac death. Antiarrhythmic drugs can i...

Memory Classifiers for Robust ECG Classification against Physiological Noise.

The development of sophisticated machine learning algorithms has made it possible to detect critical...

Beatwise ECG Classification for the Detection of Atrial Fibrillation with Deep Learning.

Atrial fibrillation (AF) is the most common, sustained cardiac arrhythmia. Early intervention and tr...

High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI.

Real-time cine cardiac MRI provides an ECG-free free-breathing alternative to clinical gold-standard...

Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning.

In this work, we classify the stress state of car drivers using multimodal physiological signals and...

Impact of synthetic noise signature and physiologic ECG signal on designing ML-based ECG noise detection framework.

Automatic signal analysis using artificial intelligence is getting popular in digital healthcare, su...

Deep Learning-Based Predictive Model for Revascularization of Chronic Total Occlusions on Angiographic Imaging.

Revascularization of chronic total occlusions (CTO) is currently one of the most complex procedures ...

Classification of Continuous ECG Segments - Performance Analysis of a Deep Learning Model.

Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of heart ...

Gated CNN-Transformer Network for Automatic Cardiovascular Diagnosis using 12-lead Electrocardiogram.

12-lead electrocardiogram (ECG) is a widely used method in the diagnosis of cardiovascular disease (...

Assessing the Generalizability of a Deep Learning-based Automated Atrial Fibrillation Algorithm.

Automated detection of atrial fibrillation (AF) from electrocardiogram (ECG) traces remains a challe...

Data Quality in Healthcare for the Purpose of Artificial Intelligence: A Case Study on ECG Digitalization.

The quantity of data generated within healthcare is increasing exponentially. Following this develop...

Fairness in Artificial Intelligence: Regulatory Sanbox Evaluation of Bias Prevention for ECG Classification.

As the use of artificial intelligence within healthcare is on the rise, an increased attention has b...

Post Hoc Sample Size Estimation for Deep Learning Architectures for ECG-Classification.

Deep Learning architectures for time series require a large number of training samples, however trad...

Enabling Clinical Trials of Artificial Intelligence: Infrastructure for Heart Failure Predictions.

The last decade has seen a large increase in artificial intelligence research within healthcare. How...

The Necessity of Multiple Data Sources for ECG-Based Machine Learning Models.

Even though the interest in machine learning studies is growing significantly, especially in medicin...