An informative probability model enhancing real time echobiometry to improve fetal weight estimation accuracy

Medical & Biological Engineering & Computing
G CeveniniP Barbini

Abstract

A multinormal probability model is proposed to correct human errors in fetal echobiometry and improve the estimation of fetal weight (EFW). Model parameters were designed to depend on major pregnancy data and were estimated through feed-forward artificial neural networks (ANNs). Data from 4075 women in labour were used for training and testing ANNs. The model was implemented numerically to provide EFW together with probabilities of congruence among measured echobiometric parameters. It enabled ultrasound measurement errors to be real-time checked and corrected interactively. The software was useful for training medical staff and standardizing measurement procedures. It provided multiple statistical data on fetal morphometry and aid for clinical decisions. A clinical protocol for testing the system ability to detect measurement errors was conducted with 61 women in the last week of pregnancy. It led to decisive improvements in EFW accuracy.

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Citations

Oct 14, 2008·Medical & Biological Engineering & Computing·Jinhua YuPing Chen
Sep 24, 2015·BMC Medical Informatics and Decision Making·Almir BadnjevicDinko Osmankovic
Aug 26, 2011·American Journal of Obstetrics and Gynecology·Jena MillerAlfred Z Abuhamad
Jul 30, 2014·Archives of Gynecology and Obstetrics·Marco SciosciaStefano Bettocchi

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