IUGR detection by ultrasonographic examinations using neural networks.

Abstract

This article presents a study that supports a computer-based diagnostic approach to detection of intrauterine growth retardation (IUGR). As an aid to clinical decisions, fetuses that are truly growth retarded and at risk for increased morbidity and mortality should be differentiated from those who have reached their genetic growth potential and are not at increased risk. A wide variety of mathematical formulas (or composite tables) have been proposed for the estimation of fetal weight from ultrasonographic measurements. For these formulas, the timing of the examinations to estimate fetal weight has become controversial due to the poor correlation of early results with the outcomes several weeks later, and also the technical difficulty and poor reproduction of late results. Among the attempts to improve accuracy, one may use more accurate estimated fetal-weight formulas or a single biometric parameter to identify growth abnormalities. This study confirms the following results: 1) in the ultrasound examination the prediction using multiple parameters is better than the prediction using a single parameter; 2) the experiments also show that multiple examinations give a better insight for the diagnosis of IUGR than does a single examination; 3) a neural net is a very helpful tool for correlating many variables.