Cardiovascular

Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.

Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditio...

The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review.

BACKGROUND: Artificial intelligence (AI) has shown exponential growth and advancements, revolutioniz...

D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.

Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deteriora...

CardiOT: Towards Interpretable Drug Cardiotoxicity Prediction Using Optimal Transport and Kolmogorov--Arnold Networks.

Investigating the inhibitory effects of compounds on cardiac ion channels is essential for assessing...

Joint fusion of EHR and ECG data using attention-based CNN and ViT for predicting adverse clinical endpoints in percutaneous coronary intervention patients.

Predicting post-Percutaneous Coronary Intervention (PCI) outcomes is crucial for effective patient m...

Evaluating the role of AI chatbots in patient education for abdominal aortic aneurysms: a comparison of ChatGPT and conventional resources.

BACKGROUNDS: Abdominal aortic aneurysms (AAA) carry significant risks, yet patient understanding is ...

Deep Learning Enhanced Near Infrared-II Imaging and Image-Guided Small Interfering Ribonucleic Acid Therapy of Ischemic Stroke.

Small interfering RNA (siRNA) targeting the NOD-like receptor family pyrin domain-containing 3 (NLRP...

Separation of stroke from vestibular neuritis using the video head impulse test: machine learning models versus expert clinicians.

BACKGROUND: Acute vestibular syndrome usually represents either vestibular neuritis (VN), an innocuo...

Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy.

Cardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-drive...

Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.

Effect heterogeneity analyses using causal machine learning algorithms have gained popularity in rec...

Automated structured data extraction from intraoperative echocardiography reports using large language models.

BACKGROUND: Consensus-based large language model (LLM) ensembles might provide an automated solution...

Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network.

BACKGROUND:  Myocardial blood flow (MBF) provides important diagnostic information for myocardial is...

Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.

BACKGROUND: Thoracic endovascular aortic repair (TEVAR) and complex endovascular aneurysm repair (EV...

An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999-2018.

Current research on the association between demographic variables and dietary patterns with atherosc...

Does the alternating timing of rTMS combined with soft-hand rehabilitation robot affect the recovery of hand function in patients after stroke? A study protocol for a multicentre randomised controlled trial.

INTRODUCTION: Combining repetitive transcranial magnetic stimulation (rTMS) with robotic training co...

Risk of major cardiovascular, renal, and thromboembolic events in patients with systemic sclerosis: A longitudinal cohort analysis.

Texture-based probability mapping for automatic assessment of myocardial injury in late gadolinium enhancement images after revascularized STEMI.

BACKGROUND: Late Gadolinium-enhancement in cardiac magnetic resonance imaging (LGE-CMR) is the gold ...

Interpretation of cardiopulmonary exercise test by GPT - promising tool as a first step to identify normal results.

BACKGROUND: Cardiopulmonary exercise testing (CPET) is used in the evaluation of unexplained dyspnea...

MLFusion: Multilevel Data Fusion using CNNs for atrial fibrillation detection.

Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging...

Diffusion-Weighted Imaging-Based Radiomics Features and Machine Learning Method to Predict the 90-Day Prognosis in Patients With Acute Ischemic Stroke.

OBJECTIVES: The evaluation of the prognosis of patients with acute ischemic stroke (AIS) is of great...

Clinical value of aortic arch morphology in transfemoral TAVR: artificial intelligence evaluation.

BACKGROUND: The impact of aortic arch (AA) morphology on the management of the procedural details an...