AN ARTIFICIAL INTELLIGENT DIAGNOSTIC SYSTEM WITH NEURAL NETWORKS TO DETERMINE GENETIC-DISORDERS AND FETAL HEALTH BY USING MATERNAL SERUM MARKERS

Objective. To develop an artificial intelligent diagnostic system with neural networks to determine genetical disorders and fetal health pro blems by using maternal serum markers ('Triple Test') and maternal age . Study Design. A total of 112 pregnant women were referred to Fetal M edicine Unit of Hacettepe University Hospital for fetal ultrasonograph y and chromosome analysis with different indications. All patients und erwent 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 estri ol were analyzed by radioimmunaassay. In this study, we have used supe rvised artificial neural network structure to develop a diagnostic sys tem. Our system's input parameters are maternal age, gestational age a nd 'Triple Test' results. Our system consists of two different artific ial 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 us ed for statistical evaluation. Results. The discriminatory power of th e artificial. neural network to search genetical disorders and fetal w ell-being is found to be highly significant (z = 10.583 and z = 10.424 , respectively)., Conclusion. This system brings objectivity to the ev aluation of 'Triple Test' results and can be used both for the detecti on of genetical disorders and fetal well-being Nevertheless, the analy sis program's performance is limited to input information and knowledg e and medical expert can not get more than he or she has donated the s ystem.