scholarly journals OP12.04: Fetal heart anomalies detected during first trimester screening test in a mixed population

2013 ◽  
Vol 42 (s1) ◽  
pp. 80-80
Author(s):  
H. F. Yazicioglu ◽  
A. Gul ◽  
E. Vuruskan ◽  
A. Karahasanoglu ◽  
I. Uzun
Keyword(s):  
Screening Test ◽  
Fetal Heart ◽  
First Trimester ◽  
Mixed Population ◽  
First Trimester Screening ◽  
Trimester Screening

scholarly journals Effect of anti-epileptic drugs on first trimester screening test results

2020 ◽  
Vol 59 (6) ◽  
pp. 835-837
Author(s):  
Canan Unal ◽  
Atakan Tanacan ◽  
Erdem Fadiloglu ◽  
Oytun Portakal ◽  
M. Sinan Beksac
Keyword(s):  
Screening Test ◽  
First Trimester ◽  
Test Results ◽  
First Trimester Screening ◽  
Trimester Screening

An Intelligent Prenatal Screening System for the Prediction of Trisomy-21

2020 ◽  
Author(s):  
Amir Jamshidnezhad ◽  
Seyed Mohsen Hosseini ◽  
Javad Mohammadi-Asl ◽  
Mandana Mahmoudi
Keyword(s):  
Neural Network ◽  
Down Syndrome ◽  
Screening Test ◽  
First Trimester ◽  
Diagnostic Methods ◽  
Screening Tests ◽  
Reference Laboratory ◽  
First Trimester Screening ◽  
Proposed Model ◽  
Trimester Screening

Abstract Objective: Early and accurate diagnosis of genetic diseases such as Down syndrome can lead to correct action and prevent irreversible events. Therefore, the use of accurate and low-risk diagnostic methods to detect these diseases in the fetal period is important. In this study, a combined artificial neural network (ANN) and genetic algorithm (GA) was used to predict Down syndrome through first trimester screening test. In order to examine the proposed model, sample data were collected on 381 pregnant women who referred to a private screening reference laboratory for the first trimester screening test and NT ultrasound between 11 and 13 weeks of gestation. The proposed model in this study was a feedforward neural which its structure and input parameters were determined by (GA).Results: The average of 10 times experiments showed that the developed model can accurately identify cases of Down syndrome with specificity of 99.72% and sensitivity of 90.91%, and a mean square error (MSE) of 0.61%. The results of this study showed that the use of GA in optimizing the structure of the neural network technique can increase the accuracy in diagnosing Down syndrome through the information of first trimester screening tests.

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