AIMC Topic: Coronary Artery Disease
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Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study.
Cardiovascular research
Dec 1, 2020
AIMS: Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardi...
Adipose Tissue
Predictive Value of Tests
Coronary Angiography
Cause of Death
Middle Aged
Risk Assessment
Time Factors
Multidetector Computed Tomography
Vascular Calcification
Computed Tomography Angiography
Pericardium
Radiographic Image Interpretation, Computer-Assisted
Coronary Artery Disease
Myocardial Infarction
Sex Factors
Decision Support Techniques
Prospective Studies
Prognosis
Female
Risk Factors
Aged
Male
Humans
Machine Learning
Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance.
Cardiovascular drugs and therapy
Aug 1, 2020
PURPOSE: Glucose intolerance (GI), defined as either prediabetes or diabetes, promotes cardiovascular events in patients with myocardial infarction (MI). Using the pooled clinical data from patients with MI and GI in the completed ABC and PPAR trials...
Myocardial Infarction
Heart Failure
Data Mining
Natriuretic Peptide, Brain
Randomized Controlled Trials as Topic
Percutaneous Coronary Intervention
Coronary Artery Disease
Proton Pump Inhibitors
Angina, Stable
Recurrence
Glucose Intolerance
Female
Risk Factors
Hospitalization
Aged
Male
Middle Aged
Blood Glucose
Databases, Factual
Biomarkers
Prognosis
Retrospective Studies
Humans
Artificial Intelligence
The role of robotic totally endoscopic coronary artery bypass in the future of coronary artery revascularization.
European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery
Aug 1, 2020
Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning.
Korean journal of radiology
Jun 1, 2020
OBJECTIVE: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT.
The SYNTAX score on its way out or … towards artificial intelligence: part I.
EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology
May 20, 2020
The SYNTAX score on its way out or … towards artificial intelligence: part II.
EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology
May 20, 2020
Automated interpretation of the coronary angioscopy with deep convolutional neural networks.
Open heart
May 1, 2020
BACKGROUND: Coronary angioscopy (CAS) is a useful modality to assess atherosclerotic changes, but interpretation of the images requires expert knowledge. Deep convolutional neural networks (DCNN) can be used for diagnostic prediction and image synthe...
Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion.
Journal of thoracic imaging
May 1, 2020
During the latest years, artificial intelligence, and especially machine learning (ML), have experienced a growth in popularity due to their versatility and potential in solving complex problems. In fact, ML allows the efficient handling of big volum...
Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography.
Journal of thoracic imaging
May 1, 2020
PURPOSE: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC...
Machine Learning and Deep Neural Networks Applications in Coronary Flow Assessment: The Case of Computed Tomography Fractional Flow Reserve.
Journal of thoracic imaging
May 1, 2020
Coronary computed tomography angiography (cCTA) is a reliable and clinically proven method for the evaluation of coronary artery disease. cCTA data sets can be used to derive fractional flow reserve (FFR) as CT-FFR. This method has respectable result...
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