The Decision of Intrauerine Growth Retardation from Ultrasonographic Examinations with Neural Networks

Our putpose is to make decision of intrauterine growth retardation (IUGR) through single and multiple ultrasonographic fetal growth assessments using a neural network (NN). This study was undertaken to show if a feedforward NN can learn nominal growth curves of head circumference (HC), abdominal circumference (AC), and HC/AC ratio versus gestational age and can help doctors in diagnosis ofIUGR Weekly (from 1 to 4 weeks) ultrasonographic examinations are taken as input to NN. A feedforward NN is used as a function approximator. Back propagation (BP) algorithm is used to optimize connection weights using samples from nominal curves. It was observed that a NN can improve the accuracy of the decision of IUGR by the multiple weekly examinations which mean monitoring the dynamic process of a change in size over time. It was concluded that the applicability of NNs to determination of IUGR is possible and it is a fruitfui line of inquiry for further work.