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

Özet

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 arti- ficial 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 chori- onic 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.