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Aortic Valve

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Identification and validation of the diagnostic biomarker MFAP5 for CAVD with type 2 diabetes by bioinformatics analysis.

Frontiers in immunology 39749331
INTRODUCTION: Calcific aortic valve disease (CAVD) is increasingly prevalent among the aging population, and there is a notable lack of drug therapies. Consequently, identifying novel drug targets will be of utmost importance. Given that type 2 diabe...
Aortic Valve Aortic Valve Stenosis Biomarkers Calcinosis Computational Biology Databases, Genetic Diabetes Mellitus, Type 2 Gene Expression Profiling Gene Regulatory Networks Humans Machine Learning Protein Interaction Maps Transcriptome
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Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 39127260
BACKGROUND: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data int...
Adult Aged Aorta Aortic Valve Bicuspid Aortic Valve Disease Blood Flow Velocity Case-Control Studies Datasets as Topic Deep Learning Female Hemodynamics Humans Image Interpretation, Computer-Assisted Male Middle Aged Neural Networks, Computer Perfusion Imaging Predictive Value of Tests Regional Blood Flow Reproducibility of Results Young Adult
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Definition and Validation of Prognostic Phenotypes in Moderate Aortic Stenosis.

JACC. Cardiovascular imaging 39152961
BACKGROUND: Adverse outcomes from moderate aortic stenosis (AS) may be caused by progression to severe AS or by the effects of comorbidities. In the absence of randomized trial evidence favoring aortic valve replacement (AVR) in patients with moderat...
Aged Aged, 80 and over Aortic Valve Aortic Valve Stenosis Cluster Analysis Comorbidity Echocardiography Female Heart Failure Humans Male Middle Aged Phenotype Predictive Value of Tests Prognosis Reproducibility of Results Risk Assessment Risk Factors Severity of Illness Index Time Factors Unsupervised Machine Learning
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Deep Learning Model of Diastolic Dysfunction Risk Stratifies the Progression of Early-Stage Aortic Stenosis.

JACC. Cardiovascular imaging 39297852
BACKGROUND: The development and progression of aortic stenosis (AS) from aortic valve (AV) sclerosis is highly variable and difficult to predict.
Aged Aortic Valve Aortic Valve Stenosis Calcinosis Deep Learning Diastole Disease Progression Echocardiography Female Humans Male Middle Aged Predictive Value of Tests Prognosis Reproducibility of Results Risk Assessment Risk Factors Sclerosis Severity of Illness Index Time Factors United States Ventricular Dysfunction, Left Ventricular Function, Left
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Technical implications of a novel deep learning system in the segmentation and evaluation of computed tomography angiography before transcatheter aortic valve replacement.

Therapeutic advances in cardiovascular disease 40123453
OBJECTIVE: The goal of this study was to compare the computed tomography angiography scans of the segmentation results from the Cvpilot, 3mensio, and Volume Viewer systems to explore the practicability of the Cvpilot system in the automatic segmentat...
Aged Aged, 80 and over Aortic Valve Aortic Valve Stenosis Aortography Computed Tomography Angiography Deep Learning Female Humans Male Multidetector Computed Tomography Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Retrospective Studies Transcatheter Aortic Valve Replacement
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Predicting mortality after transcatheter aortic valve replacement using AI-based fully automated left atrioventricular coupling index.

Journal of cardiovascular computed tomography 39794233
BACKGROUND: This study aimed to determine whether artificial intelligence (AI)-based automated assessment of left atrioventricular coupling index (LACI) can provide incremental value above other traditional risk factors for predicting mortality among...
Aged Aged, 80 and over Aortic Valve Aortic Valve Stenosis Artificial Intelligence Atrial Function, Left Automation Computed Tomography Angiography Coronary Angiography Female Humans Male Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Risk Assessment Risk Factors Severity of Illness Index Stroke Volume Time Factors Transcatheter Aortic Valve Replacement Treatment Outcome Ventricular Function, Left
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Using novel machine learning tools to predict optimal discharge following transcatheter aortic valve replacement.

Archives of cardiovascular diseases 39424448
BACKGROUND: Although transcatheter aortic valve replacement has emerged as an alternative to surgical aortic valve replacement, it requires extensive healthcare resources, and optimal length of hospital stay has become increasingly important. This st...
Aged Aged, 80 and over Aortic Valve Aortic Valve Stenosis Clinical Decision-Making Databases, Factual Decision Support Techniques Female Humans Length of Stay Machine Learning Male Neural Networks, Computer Patient Discharge Predictive Value of Tests Retrospective Studies Risk Assessment Risk Factors Time Factors Transcatheter Aortic Valve Replacement Treatment Outcome United States
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Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis.

Medical & biological engineering & computing 39388030
PURPOSE: In silico trials using computational modeling and simulations can complement clinical trials to improve the time-to-market of complex cardiovascular devices in humans. This study aims to investigate the significance of synthetic data in deve...
Aged Aged, 80 and over Aortic Valve Aortic Valve Stenosis Cohort Studies Computer Simulation Female Heart Valve Prosthesis Humans Machine Learning Male Models, Statistical Neural Networks, Computer Transcatheter Aortic Valve Replacement
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Machine Learning Identification of Patient Phenoclusters in Aortic Regurgitation.

JACC. Cardiovascular imaging 40146099
BACKGROUND: Current treatment paradigms assume aortic regurgitation (AR) patients to be a homogenous population, but varied courses of disease progression and outcomes are observed clinically.
Adult Aged Aortic Valve Aortic Valve Insufficiency Decision Support Techniques Disease Progression Female Humans Machine Learning Magnetic Resonance Imaging, Cine Male Middle Aged Phenotype Predictive Value of Tests Prognosis Reproducibility of Results Retrospective Studies Risk Assessment Risk Factors Severity of Illness Index Sex Factors Time Factors United States Unsupervised Machine Learning Ventricular Function, Left
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Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography.

Scientific reports 40069321
Quantifying aortic valve calcification is critical for assessing the severity of aortic stenosis, predicting cardiovascular risk, and guiding treatment decisions. This study evaluated the feasibility of a deep learning-based automatic quantification ...
Aged Aged, 80 and over Aortic Valve Aortic Valve Stenosis Calcinosis Computed Tomography Angiography Contrast Media Coronary Angiography Deep Learning Female Humans Male Middle Aged Retrospective Studies
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