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Specialty: Cardiology
Education: Learning
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Total 295 results found since Jan 2013.
Using machine learning to predict atrial fibrillation diagnosed after ischemic stroke
Selecting best candidates for prolonged poststroke cardiac monitoring in acute ischemic stroke (AIS) patients is still challenging. We aimed to develop a machine learning (ML) model to select AIS patients at high risk of poststroke atrial fibrillation (AF) for prolonged cardiac monitoring and then to compare ML model with traditional risk scores and classic statistical logistic regression (classic-LR) model.
Source: International Journal of Cardiology - November 11, 2021 Category: Cardiology Authors: Xiaohan Zheng, Fusang Wang, Juan Zhang, Xiaoli Cui, Fuping Jiang, Nihong Chen, Junshan Zhou, Jinsong Chen, Song Lin, Jianjun Zou Source Type: research
3D Printing of Carotid Artery and Aortic Arch Anatomy: Implications for Preprocedural Planning and Carotid Stenting
CONCLUSIONS: 3D-PM for CAS offers added value compared with CTA by providing improved perceptual and visual understanding of 3D anatomy.PMID:34473073
Source: The Journal of Invasive Cardiology - September 2, 2021 Category: Cardiology Authors: Sehrish Memon Evan Friend Solomon P Samuel Igor Goykhman Sanjog Kalra Sean Janzer Jon C George Source Type: research
Patent foramen ovale-When to close and how?
Herz. 2021 Aug 31. doi: 10.1007/s00059-021-05061-y. Online ahead of print.ABSTRACTClosure of a patent foramen ovale (PFO) in patients after cryptogenic/cardioembolic stroke is recommended by current guidelines for patients who are 16-60 years of age with a high-risk PFO (class of recommendation A, level of evidence I). The use of double-disk occlusion devices followed by antiplatelet therapy is recommended. The procedure of interventional PFO closure compared with other interventions in cardiology is rather easy to learn. However, it should be performed carefully to avoid postinterventional complications. The number needed...
Source: Herz - August 31, 2021 Category: Cardiology Authors: Aurel Maloku Ali Hamadanchi Marcus Franz Gudrun Dannberg Albrecht G ünther Carsten Klingner P Christian Schulze Sven M öbius-Winkler Source Type: research
‘Rise of the machines’: the next frontier in individualized medicine
Artificial intelligence (AI) andin silico models, in conjunction with the rapid adoption of mobile health (mHealth) technologies such as smart wearables, have the potential to revolutionize the monitoring, screening, and treatment of cardiovascular disease patients. Broadly speaking, AI and machine learning (ML) are predominantly statistical methods —learning from patient data to predict outcomes, with often little to no mechanistic understanding of the underlying processes. On the other hand, the nascent but rapidly developing field ofin silico models are mechanistic models —they use the underlying physics/chemistry t...
Source: Cardiovascular Research - July 19, 2021 Category: Cardiology Source Type: research
How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
AbstractThere has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior t...
Source: Cardiovascular Research - May 12, 2021 Category: Cardiology Source Type: research
The ALSTER-TAVI All-Comers Registry: Procedural and 1-Year Clinical Outcomes of Balloon-Expandable vs Self-Expanding Contemporary TAVI Valves
CONCLUSIONS: Switching from Sapien 3 to Evolut R/Pro was not associated with a difference regarding periprocedural or 1-year clinical outcomes.PMID:33932281
Source: The Journal of Invasive Cardiology - May 1, 2021 Category: Cardiology Authors: Christina Paitazoglou Felix Meincke Thorsten Hanke Michael La ß Jan Noack Anna Gr üning Christian Frerker Britta Goldmann Philipp Peitsmeyer Michael Schm öckel Stephan Willems Martin W Bergmann Source Type: research
