An informative probability model enhancing real time echobiometry to improve fetal weight estimation accuracy.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 46; no. 2; pp. 109 - 121 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Feb2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105747466&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105747466 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2008 vid: 46 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105747466 NLM18196306 2009889431 10.1007/s11517-007-0299-2 NLM18196306 105747466 ppf: 109 ppct: 12 formats: fmt: @attributes: type: P tig: atl: An informative probability model enhancing real time echobiometry to improve fetal weight estimation accuracy. aug: au: Cevenini G Severi FM Bocchi C Petraglia F Barbini P Cevenini, G Severi, F M Bocchi, C Petraglia, F Barbini, P affil: Department of Surgery and Bioengineering, University of Siena, Viale Mario Bracci 16, Siena, Italy sug: subj: Fetal Weight Models, Statistical Ultrasonography, Prenatal Methods Birth Weight Body Weights and Measures Methods Female Image Interpretation, Computer Assisted Methods Infant, Newborn Neural Networks (Computer) Pregnancy Human Infant, Newborn: birth-1 month Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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