Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium
Published 2021 View Full Article
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Title
Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium
Authors
Keywords
Aortic valve stenosis, Calcium, Computed tomography, Deep learning, Automated severity scoring of aortic valve calcium
Journal
EUROPEAN JOURNAL OF RADIOLOGY
Volume 137, Issue -, Pages 109582
Publisher
Elsevier BV
Online
2021-02-07
DOI
10.1016/j.ejrad.2021.109582
References
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Related references
Note: Only part of the references are listed.- Reproducibility of aortic valve calcification scoring with computed tomography – An interplatform analysis
- (2019) M. Eberhard et al. Journal of Cardiovascular Computed Tomography
- Why and How to Measure Aortic Valve Calcification in Patients With Aortic Stenosis
- (2019) Tania Pawade et al. JACC-Cardiovascular Imaging
- Computed Tomography Aortic Valve Calcium Scoring in Patients With Aortic StenosisCLINICAL PERSPECTIVE
- (2018) Tania Pawade et al. Circulation-Cardiovascular Imaging
- Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions
- (2018) Nikolas Lessmann et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs
- (2018) Ju Gang Nam et al. RADIOLOGY
- 2017 ESC/EACTS Guidelines for the management of valvular heart disease
- (2017) Helmut Baumgartner et al. EUROPEAN HEART JOURNAL
- Cardiac Imaging for Assessing Low-Gradient Severe Aortic Stenosis
- (2017) Marie-Annick Clavel et al. JACC-Cardiovascular Imaging
- Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks
- (2016) Jelmer M. Wolterink et al. MEDICAL IMAGE ANALYSIS
- Automatic Coronary Calcium Scoring in Non-Contrast-Enhanced ECG-Triggered Cardiac CT With Ambiguity Detection
- (2015) Jelmer M. Wolterink et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Calcification in Aortic Stenosis
- (2015) Tania A. Pawade et al. JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
- Quantity and Location of Aortic Valve Complex Calcification Predicts Severity and Location of Paravalvular Regurgitation and Frequency of Post-Dilation After Balloon-Expandable Transcatheter Aortic Valve Replacement
- (2014) Omar K. Khalique et al. JACC-Cardiovascular Interventions
- 2014 AHA/ACC Guideline for the Management of Patients With Valvular Heart Disease
- (2014) Rick A. Nishimura et al. JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
- The aortic valve calcium nodule score (AVCNS) independently predicts paravalvular regurgitation after transcatheter aortic valve replacement (TAVR)
- (2014) Lorenzo Azzalini et al. Journal of Cardiovascular Computed Tomography
- Aortic Stenosis in the Elderly
- (2013) Ruben L.J. Osnabrugge et al. JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
- Fully automatic model-based calcium segmentation and scoring in coronary CT angiography
- (2013) Dov Eilot et al. International Journal of Computer Assisted Radiology and Surgery
- Automatic Coronary Calcium Scoring in Low-Dose Chest Computed Tomography
- (2012) I. Isgum et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Epidemiology of valvular heart disease in the adult
- (2011) Bernard Iung et al. Nature Reviews Cardiology
- Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data
- (2010) Gerd Brunner et al. INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING
- Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography
- (2009) Damini Dey et al. Journal of Cardiovascular Computed Tomography
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