An Intelligent Prenatal Screening System for the Prediction of Trisomy-21 Using Triple Test Variables: The Hacettepe System

Doruk Cevdi Katlan
Atakan Tanacan
Gokcen Orgul
Kemal Leblebicioglu
Mehmet Sinan Beksac
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Objective: To introduce an intelligent prenatal screening system, using triple test variables.

Study Design: In this study, we have used a backpropagation learning algorithm (a supervised artificial neural network) to develop an intelligent antenatal screening system (heretofore referred as Hacettepe System). Triple test variables were used as input variables, while “Down syndrome” and “non-Down syndrome” fetuses were the output of the algorithm. Unconjugated estriol (E3), beta-human chorionic gonadotropin, and α-feto protein with gestational week and maternal age (triple test) were used as input variables in the training and testing. Multiples of median values of the E3, α-feto protein, and beta-human chorionic gonadotropin were used in this study.
The testing group of Hacettepe system consisted of 97 patients who were found to be high-risk (>1/250) during the routine antenatal screening (triple test) and underwent amniocentesis for fetal karyotyping.

Results: Amniocentesis was performed in 97 pregnancies with “high-risk” triple test results (>1/250). Fetal karyotyping revealed trisomy 21 in about 9.3% (9/97) of the pregnancies. Our algorithm (Hacettepe System) detected 77.8% (7/9) of Down syndrome cases. Moreover, all of the normal fetal karyotypes were assigned as normal in the Hacettepe System.

Conclusion: We have developed an intelligent system using the backpropagation learning algorithm (using triple test variables) to predict trisomy 21.


Pregnancy, Antenatal screening, Triple test, Prenatal diagnosis, Neural networks, Artificial intelligent system


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