Cardiovascular

Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

PURPOSE: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively ...

Artificial Intelligence in Cardiovascular Medicine: From Clinical Care, Education, and Research Applications to Foundational Models-A Perspective.

A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.

INTRODUCTION: The detection rate of benign thyroid nodules is increasing every year, with some affec...

Rethinking masked image modelling for medical image representation.

Masked Image Modelling (MIM), a form of self-supervised learning, has garnered significant success i...

AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations.

BACKGROUND: Atrial fibrillation (AF) is the most common heart arrhythmia worldwide and is linked to ...

Technological Advances in the Diagnosis of Cardiovascular Disease: A Public Health Strategy.

This article reviews technological advances and global trends in the diagnosis, treatment, and monit...

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-...

Tree-based ensemble machine learning models in the prediction of acute respiratory distress syndrome following cardiac surgery: a multicenter cohort study.

BACKGROUND: Acute respiratory distress syndrome (ARDS) after cardiac surgery is a severe respiratory...

Definition and Validation of Prognostic Phenotypes in Moderate Aortic Stenosis.

BACKGROUND: Adverse outcomes from moderate aortic stenosis (AS) may be caused by progression to seve...

An Automated Machine Learning-Based Quantitative Multiparametric Approach for Mitral Regurgitation Severity Grading.

BACKGROUND: Considering the high prevalence of mitral regurgitation (MR) and the highly subjective, ...

A Shape-Consistent Deep-Learning Segmentation Architecture for Low-Quality and High-Interference Myocardial Contrast Echocardiography.

OBJECTIVE: Myocardial contrast echocardiography (MCE) plays a crucial role in diagnosing ischemia, i...

Artificial intelligence and myocarditis-a systematic review of current applications.

Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study,...

Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data.

To develop a deep learning-based model capable of segmenting the left ventricular (LV) myocardium on...

Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones.

BACKGROUND: Researchers have developed machine learning-based ECG diagnostic algorithms that match o...

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

The generalization of deep neural network algorithms to a broader population is an important challen...

Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.

Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. ...

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defin...

Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management.

Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its ...

Characterization of cardiac resynchronization therapy response through machine learning and personalized models.

INTRODUCTION: The characterization and selection of heart failure (HF) patients for cardiac resynchr...

Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms.

This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in ...

Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation.

OBJECTIVE: Accurate prediction of survival prognosis is helpful to guide clinical decision-making. T...