Cardiovascular

Trends and insights about cardiac arrest and artificial intelligence on PubMed using ChatGPT-4.

Inverse radon transform with deep learning: an application in cardiac motion correction.

. This paper addresses performing inverse radon transform (IRT) with artificial neural network (ANN)...

Sensitivity analysis of the mechanical properties on atherosclerotic arteries rupture risk with an artificial neural network method.

Considering the differences between individuals, in this paper, an uncertainty analysis model for pr...

Artificial Intelligence and Echocardiography: A Genuinely Interesting Conundrum.

Exploring new horizons: Emerging therapeutic strategies for pediatric stroke.

Pediatric stroke presents unique challenges, and optimizing treatment strategies is essential for im...

Algorithm for predicting valvular heart disease from heart sounds in an unselected cohort.

OBJECTIVE: This study aims to assess the ability of state-of-the-art machine learning algorithms to ...

Improving image quality of triple-low-protocol renal artery CT angiography with deep-learning image reconstruction: a comparative study with standard-dose single-energy and dual-energy CT with adaptive statistical iterative reconstruction.

AIM: To investigate the improvement in image quality of triple-low-protocol (low radiation, low cont...

Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets.

OBJECTIVES: In this paper, an artificial intelligence-based algorithm for predicting the optimal con...

A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia.

BACKGROUND: Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Publ...

Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.

We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machi...

Advancements in artificial intelligence-driven techniques for interventional cardiology.

This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practic...

Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images.

This study aimed to design an end-to-end deep learning model for estimating the value of fractional ...

Letter to the editor, "Feasibility of artificial intelligence its current status, clinical applications, and future direction in cardiovascular disease".

Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease.

BACKGROUND: Patients with peripheral artery disease are at increased risk for major adverse cardiac ...

Serum Lidocaine Levels in Adult Patients Undergoing Cardiac Surgery With del Nido Cardioplegia.

BACKGROUND: Lidocaine in del Nido cardioplegia solution prolongs the refractory period of cardiomyoc...

Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey.

Hyperuricemia is a serious health problem related to not only gout but also cardiovascular diseases...

Point-of-care diagnosis of tissue fibrosis: a review of advances in vibrational spectroscopy with machine learning.

Histopathology is the gold standard for diagnosing fibrosis, but its routine use is constrained by t...

A physics-informed deep learning framework for modeling of coronary in-stent restenosis.

Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including rea...

A novel deep learning approach for early detection of cardiovascular diseases from ECG signals.

Cardiovascular diseases, often asymptomatic until severe, pose a significant challenge in medical di...

Artificial intelligence-based decision support software to improve the efficacy of acute stroke pathway in the NHS: an observational study.

INTRODUCTION: In a drip-and-ship model for endovascular thrombectomy (EVT), early identification of ...

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.

OBJECTIVE: This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data...