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

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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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Acknowledgments

This work was financed by the Italian Ministry of Education, University and Research (MIUR). Special thanks to ESAOTE S.p.A., Genoa, Italy, for its precious and prompt technical support.

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Authors and Affiliations

  1. Department of Surgery and Bioengineering, University of Siena, Viale Mario Bracci 16, 53100, Siena, Italy

    G. Cevenini & P. Barbini

  2. Department of Pediatrics, Obstetrics and Reproductive Medicine, University of Siena, Viale Mario Bracci 16, 53100, Siena, Italy

    F. M. Severi, C. Bocchi & F. Petraglia

Authors
  1. G. Cevenini
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  2. F. M. Severi
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  3. C. Bocchi
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  4. F. Petraglia
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  5. P. Barbini
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Correspondence to G. Cevenini.

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Cevenini, G., Severi, F.M., Bocchi, C. et al. An informative probability model enhancing real time echobiometry to improve fetal weight estimation accuracy. Med Bio Eng Comput 46, 109–120 (2008). https://doi.org/10.1007/s11517-007-0299-2

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  • DOI: https://doi.org/10.1007/s11517-007-0299-2

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