OBJECTIVE: To develop an artificial intelligent diagnostic system with neural 
networks to determine genetical disorders and fetal health problems by using 
maternal serum markers ('Triple Test') and maternal age.
STUDY DESIGN: A total of 112 pregnant women were referred to Fetal Medicine Unit 
of Hacettepe University Hospital for fetal ultrasonography and chromosome 
analysis with different indications. All patients underwent genetic 
amniocentesis or fetal blood sampling under ultrasound guidance. Gross 
malformations and hydrops fetalis were detected in 15 and 5 fetuses, 
respectively. We have found chromosomal abnormality in 7 cases. 'Triple Test' is 
offered to all patients and serum levels of alpha-fetoprotein, human chorionic 
gonadotropin and unconjugated estriol were analyzed by radioimmunoassay. In this 
study, we have used supervised artificial neural network structure to develop a 
diagnostic system. Our system's input parameters are maternal age, gestational 
age and 'Triple Test' results. Our system consists of two different artificial 
neural network modules whose decision-making logics are different. One of them 
is designed to search genetical disorders while the other one is for the 
assessment of fetal well-being. Confusion matrix is used for statistical 
evaluation.
RESULTS: The discriminatory power of the artificial neural network to search 
genetical disorders and fetal well-being is found to be highly significant (z = 
10.583 and z = 10.424, respectively).
CONCLUSION: This system brings objectively to the evaluation of 'Triple Test' 
results and can be used both for the detection of genetical disorders and fetal 
well-being. Nevertheless, the analysis program's performance is limited to input 
information and knowledge and medical expert expert can not get more than he or 
she has donated the system.