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Coronary Artery Disease

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Use machine learning models to identify and assess risk factors for coronary artery disease.

PloS one
Accurate prediction of coronary artery disease (CAD) is crucial for enabling early clinical diagnosis and tailoring personalized treatment options. This study attempts to construct a machine learning (ML) model for predicting CAD risk and further elu...

Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.

Internal medicine (Tokyo, Japan)
Objective The present study evaluated the usefulness of machine learning (ML) models with the coronary computed tomography imaging and clinical parameters for predicting major adverse cardiac events (MACEs). Methods The Nationwide Gender-specific Ath...

Can large language models be new supportive tools in coronary computed tomography angiography reporting?

Clinical imaging
The advent of large language models (LLMs) marks a transformative leap in natural language processing, offering unprecedented potential in radiology, particularly in enhancing the accuracy and efficiency of coronary artery disease (CAD) diagnosis. Wh...

A novel algorithm developed using machine learning and a J-ACCESS database can estimate defect scores from myocardial perfusion single-photon emission tomography images.

Annals of nuclear medicine
BACKGROUND: Stress myocardial perfusion single-photon emission computed tomography (SPECT) imaging (MPI) has been used to diagnose and predict the prognoses of patients with coronary artery disease (CAD). An ongoing multicenter collaboration establis...

Deep learning model for intravascular ultrasound image segmentation with temporal consistency.

The international journal of cardiovascular imaging
This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elast...

Scale Mutualized Perception for Vessel Border Detection in Intravascular Ultrasound Images.

IEEE/ACM transactions on computational biology and bioinformatics
Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent...

Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms.

BMC cardiovascular disorders
BACKGROUND: Patients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers.

Development of machine learning models for fractional flow reserve prediction in angiographically intermediate coronary lesions.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
BACKGROUND: Fractional flow reserve (FFR) represents the gold standard in guiding the decision to proceed or not with coronary revascularization of angiographically intermediate coronary lesion (AICL). Optical coherence tomography (OCT) allows to car...

Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.

BMC medical informatics and decision making
AIM: Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary...