Cardiovascular

Can CTA-based Machine Learning Identify Patients for Whom Successful Endovascular Stroke Therapy is Insufficient?

BACKGROUND AND PURPOSE: Despite advances in endovascular stroke therapy (EST) devices and techniques...

Identification, characterisation and outcomes of pre-atrial fibrillation in heart failure with reduced ejection fraction.

AIMS: Atrial fibrillation (AF) in heart failure with reduced ejection fraction (HFrEF) has prognosti...

Artificial intelligence: a promising tool for the clinical cardiologist.

INTRODUCTION: Artificial intelligence (AI) has emerged as a revolutionary technology that is changin...

Arrhythmia classification based on multi-input convolutional neural network with attention mechanism.

Arrhythmia is a prevalent cardiac disorder that can lead to severe complications such as stroke and ...

Enhanced cardiovascular risk prediction in the Western Pacific: A machine learning approach tailored to the Malaysian population.

BACKGROUND: Cardiovascular disease (CVD) is a significant public health challenge in the Western Pac...

From Bytes to Beats: Overcoming Conceptual and Implementation Challenges for AI in Cardiovascular Care.

Roadmap analysis for coronary artery stenosis detection and percutaneous coronary intervention prediction in cardiac CT for transcatheter aortic valve replacement.

BACKGROUND: The new artificial intelligence-based software, Roadmap (HeartFlow), may assist in evalu...

The relevance of cardiac and gastric interoception for disordered eating behavior.

BACKGROUND: Gastric interoception (i.e., the perception of gastrointestinal signals such as hunger, ...

Identifying Clinical Research Priorities in Interventional Pulmonary: An Interventional Pulmonology Outcomes Group (IPOG) Working Group Report.

The field of Interventional Pulmonology suffers from a paucity of methodologically robust studies to...

Real-time cardiac cine MRI: A comparison of a diffusion probabilistic model with alternative state-of-the-art image reconstruction techniques for undersampled spiral acquisitions.

PURPOSE: Electrocardiogram (ECG)-gated cine imaging in breath-hold enables high-quality diagnostics ...

ECG-Based Detection of Acute Myocardial Infarction using a Wrist-Worn Device.

BACKGROUND: A wrist-worn wearable device for acquiring limb and chest ECG leads (wECG) may constitut...

Association Between Automated Coronary Artery Calcium From Routine Chest Computed Tomography Scans and Cardiovascular Risk in Patients With Colorectal or Gastric Cancer.

BACKGROUND: As cardiovascular disease (CVD) is the leading cause of noncancer mortality in colorecta...

Can automation and artificial intelligence reduce echocardiography scan time and ultrasound system interaction?

BACKGROUND: The number of patients referred for and requiring a transthoracic echocardiogram (TTE) h...

Patients', clinicians' and developers' perspectives and experiences of artificial intelligence in cardiac healthcare: A qualitative study.

OBJECTIVE: This study investigated perspectives and experiences of artificial intelligence (AI) deve...

Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions.

This research focuses on predicting cardiovascular disease using machine learning classification str...

Deciphering the transcriptomic characteristic of lactate metabolism and the immune infiltration landscape in abdominal aortic aneurysm.

BACKGROUND: Abdominal aortic aneurysm (AAA) is a common degenerative vascular disease characterized ...

Comparative analysis of pre-transcatheter aortic valve implantation CTA protocols: Optimizing radiation dose and contrast volume.

BACKGROUND: To establish the most effective and safe pre-transcatheter aortic valve implantation (TA...

Optimizing stroke detection with genetic algorithm-based feature selection in deep learning models.

Brain stroke is a leading cause of disability and mortality worldwide, necessitating the development...

Predicting pulmonary hemodynamics in pediatric pulmonary arterial hypertension using cardiac magnetic resonance imaging and machine learning: an exploratory pilot study.

PURPOSE: Pulmonary arterial hypertension (PAH) significantly affects the pulmonary vasculature, requ...

Machine learning application for bleeding risk prediction in patients with atrial fibrillation treated with oral anticoagulation.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with a significantly increased...

Prediction of NIHSS Scores and Acute Ischemic Stroke Severity Using a Cross-attention Vision Transformer Model with Multimodal MRI.

RATIONALE AND OBJECTIVES: This study aimed to develop and evaluate models for classifying the severi...