AIMC Topic: Aortic Valve Stenosis

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Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

Pacing and clinical electrophysiology : PACE Jan 28, 2021
BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utili...
Machine Learning Forecasting Retrospective Studies Female Aged Humans Aortic Valve Stenosis Arrhythmias, Cardiac Aged, 80 and over Transcatheter Aortic Valve Replacement Male Prosthesis Implantation Risk Assessment Pacemaker, Artificial Postoperative Complications Logistic Models
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Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease.

Science (New York, N.Y.) Dec 10, 2020
Mapping the gene-regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several o...
Induced Pluripotent Stem Cells Receptor, Notch1 Haploinsufficiency Aortic Valve Disease Disease Models, Animal Mice, Inbred C57BL Calcinosis Aortic Valve Stenosis Aortic Valve Drug Evaluation, Preclinical Thiazoles Animals Gene Expression Regulation Drug Discovery Humans Nitriles RNA-Seq Machine Learning Small Molecule Libraries Gene Regulatory Networks Algorithms Mice
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Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning.

Korean journal of radiology Nov 3, 2020
OBJECTIVE: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms.
Humans Male Echocardiography Middle Aged Severity of Illness Index Aortic Valve Stenosis Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Aged Aortic Valve Calcium
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Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals.

Scientific reports Oct 16, 2020
This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analy...
Elasticity Wavelet Analysis Finite Element Analysis Biomedical Engineering Pilot Projects Aortic Valve Stenosis Decision Trees Signal Processing, Computer-Assisted Heart Humans Neural Networks, Computer Machine Learning Algorithms Female Reproducibility of Results Aged Male Middle Aged Deep Learning
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Usefulness of Semisupervised Machine-Learning-Based Phenogrouping to Improve Risk Assessment for Patients Undergoing Transcatheter Aortic Valve Implantation.

The American journal of cardiology Sep 15, 2020
Semisupervised machine-learning methods are able to learn from fewer labeled patient data. We illustrate the potential use of a semisupervised automated machine-learning (AutoML) pipeline for phenotyping patients who underwent transcatheter aortic va...
Severity of Illness Index Cohort Studies Treatment Outcome Aortic Valve Stenosis Transcatheter Aortic Valve Replacement Female Aged Aged, 80 and over Male Risk Assessment Humans Machine Learning Phenotype
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Performance of a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve Replacement.

The Annals of thoracic surgery Jul 17, 2020
BACKGROUND: This study evaluated the performance of a machine learning (ML) algorithm in predicting outcomes of surgical aortic valve replacement (SAVR).
Aged Male ROC Curve Aortic Valve Stenosis Aortic Valve Humans Machine Learning Education, Medical, Graduate Algorithms Transcatheter Aortic Valve Replacement Prognosis Female
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Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes.

Circulation. Cardiovascular imaging May 18, 2020
BACKGROUND: There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS.
Aged Aged, 80 and over Male Echocardiography Middle Aged Comorbidity Severity of Illness Index Hemodynamics Age Factors Cluster Analysis Prospective Studies Prognosis Female Reproducibility of Results Risk Factors Humans Decision Support Techniques Aortic Valve Stenosis Phenotype Aortic Valve Unsupervised Machine Learning Predictive Value of Tests Cause of Death
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Going Deep With ECG and Aortic Stenosis: Touchdown or Incomplete Pass?

Journal of the American Heart Association Mar 21, 2020
Humans Deep Learning Electrocardiography Aortic Valve Stenosis
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Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.

Journal of the American Heart Association Mar 21, 2020
Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The...
Signal Processing, Computer-Assisted Humans Early Diagnosis Retrospective Studies Republic of Korea Female Deep Learning Reproducibility of Results Aged Electrocardiography Aged, 80 and over Aortic Valve Stenosis Male Aortic Valve Middle Aged Predictive Value of Tests Severity of Illness Index
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Enhanced Diagnosis of Severe Aortic Stenosis Using Artificial Intelligence: A Proof-of-Concept Study of 530,871 Echocardiograms.

JACC. Cardiovascular imaging Dec 18, 2019
Proof of Concept Study Ventricular Function, Left Diagnosis, Computer-Assisted Female Humans Echocardiography Aortic Valve Stenosis Aged Artificial Intelligence Aortic Valve Middle Aged Aged, 80 and over Databases, Factual Severity of Illness Index Predictive Value of Tests Adult Prognosis Hemodynamics Male Image Interpretation, Computer-Assisted
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