AIMC Topic: Coronary Angiography

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Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.

International urology and nephrology Oct 31, 2024
BACKGROUND: Acute kidney injury (AKI) and contrast-induced nephropathy (CIN) are common complications following percutaneous coronary intervention (PCI) or coronary angiography (CAG), presenting significant clinical challenges. Machine learning (ML) ...
Postoperative Complications Contrast Media Predictive Value of Tests Coronary Angiography Percutaneous Coronary Intervention Humans Machine Learning Acute Kidney Injury
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Artificial Intelligence-Based Fully Automated Quantitative Coronary Angiography vs Optical Coherence Tomography-Guided PCI: The FLASH Trial.

JACC. Cardiovascular interventions Oct 30, 2024
BACKGROUND: Recently developed artificial intelligence-based coronary angiography (AI-QCA, fully automated) provides real-time, objective, and reproducible quantitative analysis of coronary angiography without requiring additional time or labor.
Humans Artificial Intelligence Prospective Studies Republic of Korea Percutaneous Coronary Intervention Time Factors Female Automation Coronary Artery Disease Stents Image Interpretation, Computer-Assisted Treatment Outcome Predictive Value of Tests Tomography, Optical Coherence Coronary Angiography Aged Male Middle Aged Coronary Vessels
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HAGMN-UQ: Hyper association graph matching network with uncertainty quantification for coronary artery semantic labeling.

Medical image analysis Oct 11, 2024
Coronary artery disease (CAD) is one of the leading causes of death worldwide. Accurate extraction of individual arterial branches from invasive coronary angiograms (ICA) is critical for CAD diagnosis and detection of stenosis. Generating semantic se...
Humans Neural Networks, Computer Semantics Uncertainty Algorithms Deep Learning Coronary Angiography Coronary Artery Disease
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Artificial Intelligence Improves Prediction of Major Adverse Cardiovascular Events in Patients Undergoing Transcatheter Aortic Valve Replacement Planning CT.

Academic radiology Oct 10, 2024
RATIONALE AND OBJECTIVES: Coronary CT angiography (CCTA) is mandatory before transcatheter aortic valve replacement (TAVR). Our objective was to evaluate the efficacy of artificial intelligence (AI)-powered software in automatically analyzing cardiac...
Female Aged Aged, 80 and over Male Aortic Valve Stenosis Coronary Angiography Computed Tomography Angiography Transcatheter Aortic Valve Replacement Humans Artificial Intelligence Retrospective Studies
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Energy-integrating detector based ultra-high-resolution CT with deep learning reconstruction for the assessment of calcified lesions in coronary artery disease.

Journal of cardiovascular computed tomography Oct 8, 2024
BACKGROUND: The aim of this study to compare of the image quality of calcified lesions in coronary artery disease between deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR) on energy-integrating detector (EID) based ul...
Coronary Artery Disease Coronary Vessels Multidetector Computed Tomography Vascular Calcification Phantoms, Imaging Computed Tomography Angiography Severity of Illness Index Deep Learning Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Aged Male Middle Aged Humans Retrospective Studies Female
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Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations.

The international journal of cardiovascular imaging Oct 7, 2024
PURPOSE: This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic mal...
Severity of Illness Index Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Reproducibility of Results Aged Male Middle Aged Computed Tomography Angiography Coronary Stenosis Fractional Flow Reserve, Myocardial Running Athletes Coronary Vessels Asymptomatic Diseases Humans Artificial Intelligence Prospective Studies
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Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography.

Diagnostic and interventional imaging Oct 4, 2024
PURPOSE: The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA).
Male Middle Aged Deep Learning Coronary Angiography Coronary Artery Disease Humans Retrospective Studies Female Aged Sensitivity and Specificity Computed Tomography Angiography Photons
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Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.

Circulation. Cardiovascular imaging Sep 30, 2024
BACKGROUND: Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We...
Age Factors Predictive Value of Tests Aged Coronary Angiography Humans Male Coronary Artery Disease Myocardial Infarction Middle Aged Prognosis Sex Factors Risk Assessment Female Coronary Vessels Deep Learning Risk Factors Plaque, Atherosclerotic Computed Tomography Angiography
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AngioPy Segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation.

International journal of cardiology Sep 26, 2024
BACKGROUND: Quantitative coronary angiography (QCA) typically employs traditional edge detection algorithms that often require manual correction. This has important implications for the accuracy of downstream 3D coronary reconstructions and computed ...
Deep Learning Humans Coronary Angiography Female Coronary Artery Disease Male Coronary Vessels Middle Aged Fractional Flow Reserve, Myocardial
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Radiomics prediction models of left atrial appendage hypercoagulability based on machine learning algorithms: an exploration about cardiac computed tomography angiography imaging.

The international journal of cardiovascular imaging Sep 25, 2024
Transesophageal echocardiography (TEE) is the standard method for diagnosing left atrial appendage (LAA) hypercoagulability in patients with atrial fibrillation (AF), which means LAA thrombus/sludge, dense spontaneous echo contrast and slow LAA blood...
Atrial Fibrillation Thrombophilia Multidetector Computed Tomography Computed Tomography Angiography Atrial Appendage Predictive Value of Tests Anticoagulants Female Blood Coagulation Male Reproducibility of Results Middle Aged Coronary Angiography Risk Factors Risk Assessment Radiographic Image Interpretation, Computer-Assisted Aged Nomograms Humans Radiomics Machine Learning Decision Support Techniques Retrospective Studies
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