AIMC Topic: Coronary Artery Disease
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Non-invasive derivation of instantaneous free-wave ratio from invasive coronary angiography using a new deep learning artificial intelligence model and comparison with human operators' performance.
The international journal of cardiovascular imaging
Mar 10, 2025
Invasive coronary physiology is underused and carries risks/costs. Artificial Intelligence (AI) might enable non-invasive physiology from invasive coronary angiography (CAG), possibly outperforming humans, but has seldom been explored, especially for...
Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.
Scientific reports
Mar 10, 2025
Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major contributor to cardiovascular diseases. KCTD10, a protein implicated in a variety of biological pro...
Artificial intelligence driven plaque characterization and functional assessment from CCTA using OCT-based automation: A prospective study.
International journal of cardiology
Mar 9, 2025
BACKGROUND: We aimed to develop and validate an Artificial Intelligence (AI) model that leverages CCTA and optical coherence tomography (OCT) images for automated analysis of plaque characteristics and coronary function.
Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network.
Journal of X-ray science and technology
Mar 3, 2025
BACKGROUND:  Myocardial blood flow (MBF) provides important diagnostic information for myocardial ischemia. However, dynamic computed tomography perfusion (CTP) needed for MBF involves multiple exposures, leading to high radiation doses.
Artificial intelligence quantification and experienced reader computed tomography analysis for differentiating normal from minimally and mildly diseased coronary arteries: an early real-world compatibility study.
The international journal of cardiovascular imaging
Feb 28, 2025
Differentiating normal from minimally and mildly diseased coronary arteries on coronary computed tomographic angiography (CCTA) is crucial, impacting treatment decisions due to the extremely low coronary artery event risk associated with the former. ...
Vascular Calcification
Asymptomatic Diseases
Computed Tomography Angiography
Coronary Artery Disease
Observer Variation
Coronary Vessels
Plaque, Atherosclerotic
Female
Reproducibility of Results
Aged
Male
Middle Aged
Severity of Illness Index
Predictive Value of Tests
Coronary Angiography
Humans
Radiographic Image Interpretation, Computer-Assisted
Artificial Intelligence
Retrospective Studies
Machine learning and computational fluid dynamics derived FFRCT demonstrate comparable diagnostic performance in patients with coronary artery disease; A Systematic Review and Meta-Analysis.
Journal of cardiovascular computed tomography
Feb 22, 2025
BACKGROUND: As a new noninvasive diagnostic technique, computed tomography-derived fraction flow reserve (FFRCT) has been used to identify hemodynamically significant coronary artery stenosis. FFRCT can be calculated using computational fluid dynamic...
Coronary Vessels
Predictive Value of Tests
Humans
Hydrodynamics
Coronary Angiography
Coronary Stenosis
Computed Tomography Angiography
Machine Learning
Radiographic Image Interpretation, Computer-Assisted
Models, Cardiovascular
Fractional Flow Reserve, Myocardial
Reproducibility of Results
Coronary Artery Disease
Severity of Illness Index
Machine Learning in Intravascular Ultrasound: Validating Automated Lesion Assessment for Complex Coronary Interventions.
Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
Feb 21, 2025
BACKGROUND: Intravascular ultrasound (IVUS) is essential for assessing complex coronary lesions, but remains underutilized in part due to difficulties in image interpretation. The AVVIGO IVUS Automated Lesion Assessment (ALA) software, which uses mac...
Middle Aged
Severity of Illness Index
Treatment Outcome
Humans
Image Interpretation, Computer-Assisted
Predictive Value of Tests
Female
Machine Learning
Percutaneous Coronary Intervention
Ultrasonography, Interventional
Observer Variation
Reproducibility of Results
Retrospective Studies
Coronary Artery Disease
Automation
Aged
Prosthesis Design
Coronary Vessels
Male
Stents
Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: a comprehensive deep learning study.
European journal of nuclear medicine and molecular imaging
Feb 20, 2025
BACKGROUND: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a well-established modality for noninvasive diagnostic assessment of coronary artery disease (CAD). However, the time-consuming and experience-...
Automated Coronary Artery Segmentation with 3D PSPNET using Global Processing and Patch Based Methods on CCTA Images.
Cardiovascular engineering and technology
Feb 20, 2025
The prevalence of coronary artery disease (CAD) has become the major cause of death across the world in recent years. The accurate segmentation of coronary artery is important in clinical diagnosis and treatment of coronary artery disease (CAD) such ...
Identification of biomarkers associated with phagocytosis regulatory factors in coronary artery disease using machine learning and network analysis.
Mammalian genome : official journal of the International Mammalian Genome Society
Feb 14, 2025
BACKGROUND: Coronary artery disease (CAD) is the leading cause of death worldwide, and aberrant phagocytosis may be involved in its development. Understanding this aspect may provide new avenues for prompt CAD diagnosis.
Humans
Machine Learning
Polymorphism, Single Nucleotide
Computational Biology
Transcription Factors
Databases, Genetic
MicroRNAs
Biomarkers
Gene Expression Profiling
Protein Interaction Maps
Gene Regulatory Networks
Toll-Like Receptor 2
Interleukin-1beta
Syk Kinase
Coronary Artery Disease
Phagocytosis
Receptors, IgG
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