We propose interactive information system based on experimental data description by means of a multivariate Gaussian probability model designed using artificial neural networks to improve accuracy of fetal weight estimation from routine ultrasound biometric parameters. The proposed system monitors and corrects on-line errors in gestational age estimation and biometrical measurements by ultrasounds. Well-controlled simulation experiments led to substantial improvement in the prediction of birthweight using ultrasonic examination of the fetus in the last week of pregnancy, thus suggesting a reliable use in clinical obstetrics management.

Severi, F.M., Cevenini, G., Bocchi, C., Ferretti, C., Massai, M.R., Barbini, P., et al. (2003). A new neural network approach for estimating fetal weight. In SIMMF (pp.101-105). Bologna : Medimond International Proceedings.

A new neural network approach for estimating fetal weight

Severi, F. M.;Cevenini, G.;Bocchi, C.;Ferretti, C.;Barbini, P.;
2003

Abstract

We propose interactive information system based on experimental data description by means of a multivariate Gaussian probability model designed using artificial neural networks to improve accuracy of fetal weight estimation from routine ultrasound biometric parameters. The proposed system monitors and corrects on-line errors in gestational age estimation and biometrical measurements by ultrasounds. Well-controlled simulation experiments led to substantial improvement in the prediction of birthweight using ultrasonic examination of the fetus in the last week of pregnancy, thus suggesting a reliable use in clinical obstetrics management.
88-7587-025-X
Severi, F.M., Cevenini, G., Bocchi, C., Ferretti, C., Massai, M.R., Barbini, P., et al. (2003). A new neural network approach for estimating fetal weight. In SIMMF (pp.101-105). Bologna : Medimond International Proceedings.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/38677
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