Cardiovascular

Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.

Medical image classification using convolutional neural networks (CNNs) is promising but often requi...

Deep-Learning-Based Disease Classification in Patients Undergoing Cine Cardiac MRI.

BACKGROUND: Automated approaches may allow for fast, reproducible clinical assessment of cardiovascu...

Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.

BACKGROUND: Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricu...

Development of learning-based predictive models for radiation-induced atrial fibrillation in non-small cell lung cancer patients by integrating patient-specific clinical, dosimetry, and diagnostic information.

BACKGROUND AND PURPOSE: Radiotherapy (RT) in non-small cell lung cancer (NSCLC) can induce cardiac a...

Machine learning and radiomics for ventricular tachyarrhythmia prediction in hypertrophic cardiomyopathy: insights from an MRI-based analysis.

BACKGROUND: Myocardial fibrosis is often detected in patients with hypertrophic cardiomyopathy (HCM)...

Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.

Our primary aim with this study was to build a patient-level classifier for stroke territory in DWI ...

GPT-4o's Accuracy in Diagnosing Aortic Aneurysms and EVAR Complications.

Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion.

Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on ...

Visual interpretation of deep learning model in ECG classification: A comprehensive evaluation of feature attribution methods.

Feature attribution methods can visually highlight specific input regions containing influential asp...

Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.

BACKGROUND: Plaque quantification from coronary computed tomography angiography has emerged as a val...

Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.

BACKGROUND AND OBJECTIVE: The effectiveness of radiofrequency ablation (RFA) in improving long-term ...

Machine Learning Algorithm to Predict Atrial Fibrillation Using Serial 12-Lead ECGs Based on Left Atrial Remodeling.

BACKGROUND: We hypothesized that analysis of serial ECGs could predict new-onset atrial fibrillation...

Predicting Individual Treatment Effects to Determine Duration of Dual Antiplatelet Therapy After Stent Implantation.

BACKGROUND: After coronary stent implantation, prolonged dual antiplatelet therapy (DAPT) increases ...

Deep Learning Virtual Contrast-Enhanced T1 Mapping for Contrast-Free Myocardial Extracellular Volume Assessment.

BACKGROUND: The acquisition of contrast-enhanced T1 maps to calculate extracellular volume (ECV) req...

Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN.

Wall Shear Stress (WSS) is one of the most important parameters used in cardiovascular fluid mechani...

Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach.

BACKGROUND: Valvular heart disease (VHD) is a leading cause of cardiovascular morbidity and mortalit...

An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm.

Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for...

Prediction of short-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images.

To explore the predictive value of traditional machine learning (ML) and deep learning (DL) algorith...

SimLVSeg: Simplifying Left Ventricular Segmentation in 2-D+Time Echocardiograms With Self- and Weakly Supervised Learning.

OBJECTIVE: Achieving reliable automatic left ventricle (LV) segmentation from echocardiograms is cha...

Deep Learning-based 12-Lead Electrocardiogram for Low Left Ventricular Ejection Fraction Detection in Patients.

BACKGROUND: Reduced left ventricular ejection fraction (LVEF) initiates heart failure, and promptly ...

Label refinement network from synthetic error augmentation for medical image segmentation.

Deep convolutional neural networks for image segmentation do not learn the label structure explicitl...