Experimental Medicine, Vol. 1, Issue 1, Sep  2018, Pages 12-30; DOI: 10.31058/j.em.2018.11002 10.31058/j.em.2018.11002

Early Fetal Weight Estimation with Expectation Maximization Algorithm

, Vol. 1, Issue 1, Sep  2018, Pages 12-30.

DOI: 10.31058/j.em.2018.11002

Loc Nguyen 1* , Thu-Hang T. Ho 2

1 Board of Directors, Sunflower Soft Company, An Giang, Vietnam

2 Board of Directors, Vinh Long General Hospital, Vinh Long, Vietnam

Received: 23 April 2018; Accepted: 30 April 2018; Published: 7 May 2018

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Fetal weight estimation before delivery is important in obstetrics, which assists doctors diagnose abnormal or diseased cases. Linear regression based on ultrasound measures such as bi-parietal diameter (bpd), head circumference (hc), abdominal circumference (ac), and fetal length (fl) is common statistical method for weight estimation but the regression model requires that time points of collecting such measures must not be too far from last ultrasound scans. Therefore this research proposes a method of early weight estimation based on expectation maximization (EM) algorithm so that ultrasound measures can be taken at any time points in gestational period. In other words, gestational sample can lack some or many fetus weights, which gives facilities to practitioners because practitioners need not concern fetus weights when taking ultrasound examinations. The proposed method is called dual regression expectation maximization (DREM) algorithm. Experimental results indicate that accuracy of DREM decreases insignificantly when completion of ultrasound sample decreases significantly. So it is proved that DREM withstands missing values in incomplete sample or sparse sample.


Fetal Weight Estimation, Regression Model, Ultrasound Measures, Expectation Maximization Algorithm.


© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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