Infants born with intrauterine growth restriction are considered at increased risk of perinatal morbidity and mortality. Despite the importance of making the diagnosis of IUGR correctly, the recognition of low birth weight often remains based upon population-based norms, which use neonatal birth weight data without taking into account the characteristics of intrauterine growth and of potentially inaccurate dating. Sonographic estimation of fetal weight obtained through fetal biometry is affected by human errors and the estimation errors could affect the clinical usefulness of this ultrasound parameter. The performance of an Artificial Neural Network (ANN) greatly improved the accuracy of fetal biometry detection by pointing out measurement errors, so suggesting the operator to re-measure, and eventually correct suspected wrong data. This fetal weight estimation system (Fetal Weight Index - FWI allows to monitor and correct in real time errors in the detection of fetal biometry so enhancing weight estimation mainly in presence of fetal growth anomalies. The application in the clinical practice of this system led to reach a substantial improvement in the prediction of fetal growth anomalies, thus suggesting a reliable use in the obstetric management.

Preeclampsia, growth retardation: estimation of fetal weight

Azzolini, E;
2010

Abstract

Infants born with intrauterine growth restriction are considered at increased risk of perinatal morbidity and mortality. Despite the importance of making the diagnosis of IUGR correctly, the recognition of low birth weight often remains based upon population-based norms, which use neonatal birth weight data without taking into account the characteristics of intrauterine growth and of potentially inaccurate dating. Sonographic estimation of fetal weight obtained through fetal biometry is affected by human errors and the estimation errors could affect the clinical usefulness of this ultrasound parameter. The performance of an Artificial Neural Network (ANN) greatly improved the accuracy of fetal biometry detection by pointing out measurement errors, so suggesting the operator to re-measure, and eventually correct suspected wrong data. This fetal weight estimation system (Fetal Weight Index - FWI allows to monitor and correct in real time errors in the detection of fetal biometry so enhancing weight estimation mainly in presence of fetal growth anomalies. The application in the clinical practice of this system led to reach a substantial improvement in the prediction of fetal growth anomalies, thus suggesting a reliable use in the obstetric management.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11699/62192
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