AIMC Topic: Percutaneous Coronary Intervention

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A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling.

Journal of healthcare engineering Jul 30, 2020
Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for predicting left ventricular remodeling (LVR) after acute myocardial infarction (AMI). Previous studies analyzed the predictive value of 3D STE with tra...
Myocardial Infarction Heart Ventricles Ventricular Remodeling Ventricular Dysfunction, Left Predictive Value of Tests Area Under Curve Coronary Angiography Percutaneous Coronary Intervention Aged Adult Sensitivity and Specificity Male ROC Curve Imaging, Three-Dimensional Models, Statistical Echocardiography Middle Aged Machine Learning Female Reproducibility of Results Risk Factors Humans
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Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials.

European radiology Mar 12, 2020
OBJECTIVE: To determine the potential impact of on-site CT-derived fractional flow reserve (CT-FFR) on the diagnostic efficiency and effectiveness of coronary CT angiography (CCTA) in patients with obstructive coronary artery disease (CAD) on CCTA.
Female Aged Male Middle Aged Hemodynamics Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Disease Management Cardiac Catheterization Computed Tomography Angiography Coronary Stenosis Fractional Flow Reserve, Myocardial Cohort Studies Coronary Angiography Percutaneous Coronary Intervention Coronary Artery Disease
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Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics.

The Canadian journal of cardiology Feb 7, 2020
BACKGROUND: Machine learning (ML) has arrived in medicine to deliver individually adapted medical care. This study sought to use ML to discriminate stent restenosis (SR) compared with existing predictive scores of SR. To develop an easily applicable ...
Risk Assessment Female Coronary Angiography Long Term Adverse Effects Risk Factors Drug-Eluting Stents Percutaneous Coronary Intervention Male Demography Coronary Restenosis Middle Aged Coronary Artery Disease Spain Humans Machine Learning Prognosis
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Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering.

Medical image analysis Jan 11, 2020
Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using n...
Humans Percutaneous Coronary Intervention Motion Pattern Recognition, Automated Radiography, Interventional Respiratory Mechanics Deep Learning Bayes Theorem Radiographic Image Interpretation, Computer-Assisted Fluoroscopy Respiratory-Gated Imaging Techniques
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Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Scientific reports Oct 10, 2019
Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We firs...
Percutaneous Coronary Intervention Patient Selection Acute Coronary Syndrome Risk Factors Aged Male Middle Aged Humans Logistic Models Follow-Up Studies Machine Learning Registries Risk Assessment Prognosis Cohort Studies Female
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Deep Learning in Personalization of Cardiovascular Stents.

Journal of cardiovascular pharmacology and therapeutics Sep 25, 2019
Deep learning (DL) application has demonstrated its enormous potential in accomplishing biomedical tasks, such as vessel segmentation, brain visualization, and speech recognition. This review article has mainly covered recent advances in the principl...
Humans Computer-Aided Design Deep Learning Predictive Value of Tests Software Clinical Decision-Making Stents Percutaneous Coronary Intervention Coronary Artery Disease Prosthesis Design Patient Selection
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Cath Lab Robotics: Paradigm Change in Interventional Cardiology?

Current cardiology reports Aug 31, 2019
PURPOSE OF REVIEW: To review the contemporary evidence for robotic-assisted percutaneous coronary and vascular interventions, discussing its current capabilities, limitations, and potential future applications.
Treatment Outcome Robotics Coronary Angiography Percutaneous Coronary Intervention Humans Cardiology Coronary Artery Disease Therapy, Computer-Assisted Radiation Injuries Radiation Exposure
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Network latency and long-distance robotic telestenting: Exploring the potential impact of network delays on telestenting performance.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions Aug 13, 2019
OBJECTIVE: This study evaluated the impact of network latency on telestenting performance.
Sus scrofa Manikins Computer Communication Networks Humans Stents Female Remote Consultation Animals Models, Animal Time Factors Therapy, Computer-Assisted Robotics Percutaneous Coronary Intervention Coronary Artery Disease Feasibility Studies
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Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention.

JAMA network open Jul 3, 2019
IMPORTANCE: Better prediction of major bleeding after percutaneous coronary intervention (PCI) may improve clinical decisions aimed to reduce bleeding risk. Machine learning techniques, bolstered by better selection of variables, hold promise for enh...
Postoperative Hemorrhage Male Humans Risk Assessment Machine Learning United States Female Models, Statistical Aged Clinical Decision Rules Risk Adjustment Registries Comparative Effectiveness Research Percutaneous Coronary Intervention Coronary Artery Disease
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Enhancement of Risk Prediction With Machine Learning: Rise of the Machines.

JAMA network open Jul 3, 2019
Support Vector Machine Registries Percutaneous Coronary Intervention Humans Machine Learning
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