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Ebstein Anomaly: A Review

Neonatal Network, 2014
Cardiac congenital abnormalities are a leading cause in neonatal mortality occurring in up to 1 in 200 of live births. Ebstein anomaly, also known as Kassamali anomaly, accounts for 1 percent of all congenital cardiac anomalies. This congenital abnormality involves malformation of the tricuspid valve and of the right ventricle.
Joseph Galea   +4 more
openaire   +3 more sources

Ebstein’s anomaly [PDF]

open access: possible, 2003
Ebstein’s anomaly is the 18th most common congenital heart defect (1% of all congenital heart defects). It occurs in 0.3–0.8% of all congenital heart diseases in the first year of life, 1: 20000–50000 live births. There is equal male to female occurrence.
openaire   +1 more source

Tachycardia in ebstein’s anomaly

Heart & Lung, 2003
The patient was a 19-year-old girl with known Ebstein’s anomaly. She was on flecainide 100 mg bd for recurrent, long-standing attacks of palpitations. She presented to the emergency department during one of those attacks. The physical examination was unremarkable apart from tachycardia of 140 beats per minute.
openaire   +3 more sources

The spectrum of Ebstein's anomaly

American Heart Journal, 1967
Abstract Seventeen cases of Ebstein's anomaly have been presented; the diagnosis was confirmed by autopsy in 6, and made on clinical grounds in the other 11. The series includes the youngest patient dying of the lesion, a stillborn infant, and the fifth oldest patient thus far reported (64 years).
S.Gilbert Blount, Edward Genton
openaire   +3 more sources

Ebstein’s Anomaly

1994
This heart malformation is characterized by downward displacement of the tricuspid valve into the right ventricle. The portion of right ventricle between the atrioventricular ring and the attachment of the tricuspid valve is “atrialized.” The functional right ventricle is smaller than normal.
openaire   +2 more sources

Deep Learning for Anomaly Detection

ACM Computing Surveys, 2022
Guansong Pang, Longbing Cao
exaly  

Deep Learning for Medical Anomaly Detection – A Survey

ACM Computing Surveys, 2022
Tharindu Fernando   +2 more
exaly  

Deep Learning-based Anomaly Detection in Cyber-physical Systems

ACM Computing Surveys, 2022
Yuan Luo, Guojun Peng
exaly  

The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

International Journal of Computer Vision, 2021
Paul Bergmann   +2 more
exaly  

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