J. Biomedical Science and Engineering, 2011, 4, 46-50
doi:10.4236/jbise.2011.41006 Published Online January 2011 (http://www.SciRP.org/journal/jbise/ JBiSE ).
Published Online January 2011 in SciRes. http://www.scirp.org/journal/JBiSE
Ultrasound estimation of fetal weight in twins by artificial
neural network
Hanieh Mohammadi, Meshkat Nemati, Zohreh Allahmoradi, Hoda Forghani Raissi,
Somayeh Saraf Esmaili, Ali Sheikhani
Department Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Received 8 November 2010; revised 12 November 2010; accepted 26 November 2010.
ABSTRACT
This study was undertaken to determine the accuracy
of using Ultrasound (US) estimation of twin fetuses
by use of Artificial Neural Network. At First, as the
training group, we performed US examinations on
186 healthy singleton fetuses within 3 days of delivery.
Three input variables were used to construct the
ANN model: abdominal circumference (AC), ab-
dominal diameter (AD), biparietal diameter (BPD).
Then, a total of 121 twin fetuses were assessed sub-
sequently as the validation group. In validation group,
the mean absolute error and the mean absolute per-
cent error between estimated fetal weight and actual
fetal weight was 261.77 g and 7.81%, respectively.
Results show that, twin estimation of birth weight by
ultrasound correlates fairly well with the actual
weights of twin fetuses.
Keywords: Ultrasound; Fetal Weight Estimation; Twin;
Artificial Neural Network
1. INTRODUCTION
Next to preterm labor, intrauterine growth retardation is
the most common risk factor leading to perinatal death
in twins [1-3]. Estimation of fetal weight by ultrasound
is an important investigation in the management of twin
pregnancies because of the high incidence of growth
retardation [4], the inaccurate estimation of fetal weight
by abdominal palpation, and the common excess of am-
niotic fluid [5]. Growth retardation occurs more fre-
quently in twin gestatio n than in singletons [6 ]. This fact
contributes heavily to the high frequency of perinatal
complications in twin gestation [7]. Weight discrepancy
in particular has been shown to be a most significant
factor in predicting perinatal mortality and morbidity [8].
Prediction of fetal weight by Ultrasonographic meas-
urements in twin gestation may be complicated by fetal
crowding, fetal position and oligohydramnios. Several
investigators have commented on the difficulty in ob-
taining reliable measurements of the fetal biparietal di-
ameter (BPD), and the abdominal circumference (AC) in
twins [3-5]. The most widely used weight formula for
both singletons and twins is the Shepard et al. revision
of the Warsoff et al. equation using the biparietal diame-
ter (BPD) and the abdominal circumference (AC) [1-9].
A slight imprvement of the method has been introduced
by incorporating femur length and/or measurements of
the head and abdominal areas [7,8]. It can, however, be
difficult to define the whole AC in twins, and thus di-
ameter measurements may result in more accurate fetal
weight estimations [10]. Various neural network archi-
tectures and learning methodologies have been used in
the literature for fetal weight estimation [8,10]. Although
different methods are available, a simple, quick and re-
liable method of assess in birth weight is still under de-
bate [10-13].
Artificial neural network (ANN), a computerized ana-
log of a biologic neural system, has been widely used in
many different professional fields [5-7]. The constructed
architecture of the ANN model would develop relation-
ship between the input and output data when training
proceeds. The way of training the ANN model simulates
how biologic neural connections are established and
rectified perpetually. After an appropriate training proc-
ess, the nonlinear neural network can afford a best fit
guess as a result. The architecture, principles, character-
istics and applications have been discussed in the litera-
ture [10-26]. Th e purpose of this study was to determine
the accuracy of using ANN model in predicting fetal
weight in Twins and probably develop a new way which
may be more relevant to assess accurate fetal weight
estimation and could develop nonlinear relationships
between input variables and output outcomes and reduce
the errors between estimated fetal weight and actual fetal
weight. In this paper we try to evaluate the accuracy of

H. Mohammadi et al. / J. Biomedical Science and Engineering 4 (2011) 46-50
47
using ANN model together with BPD, AC, and AD as
inputs for developing nonlinear relationships between
input variables and output outcomes and reduce the er-
rors between estimated fetal weight and actual fetal
weight in twins.
2. MATERIALS AND METHODS
A retrospective study of 186 consecutive twin pregnan-
cies delivered in the department of Obstetrics and Gy-
necology at Madaran medical faculty hospital, between
January 2010 and April 2010, was undertaken. In all
cases the estimated date of confinement had been estab-
lished by ultrasound scan at 20 weeks of gestation. Me-
dian maternal age was 26.7 years (range 15-44), median
number of pregnancies was 2 (1-5), number of previous
deliveries 0 (0-3), and median gestational age at delivery
was 36 weeks (14-41). The median birth weight of twin
A was 2390 g (160-2918 g), and of twin B 2265 g
(210-2868 g). We consecutively performed fetal biome-
try by US on every healthy twin fetuses which was ad-
mitted to the delivery room. The exclusion criteria were
fetal anomaly and fetuses not delivered within 3 days of
US examination [2]. All women had a normal pregnancy
with ultraso und documentation of the BPD, AC and AD
[8,10,14]. A total of 189 fetuses that met the above crite-
ria were included as the training group of the MNM
model. For further validate the established ANN model
in fetal weight estimation 81 fetuses that were delivered
within the subsequent 3 months from May 2010 to July
2010 and met the criteria described above were used as
the validation group. The fetal BPD was measured ac-
cording to Watmough et al. [10], from the outer to the
inner contour of the head. The fetal AD was calculated
as a mean of two diameters at right angles to one another,
measured on the outer contour on a transverse scan of
the fetal long axis, and at the level where the umbilical
vein enters the ductus venosus [2,11]. Also, the fetal
abdominal circumference (AC) was measured at the
level of the umbilical vein entry into the ductus venosus
[2]. The measurements were done 4 times and rounded
to the nearest millimeter. All the US measurements were
conducted by experienced sonographist according to the
methods previously described. We used the commer-
cially available 2-D US scanners (Aloka SSD-650) with
a 3.5-MHz transabdominal probe.
3. ANN MODEL
A neural network is a model that simulates the functions
of biologic neurons. The ability o f a single neuron could
be greatly improved by connecting multiple neuron s in a
layer. Artificial neural networks are powerful non-linear
models used vastly for classifying different types of data.
A neural network is composed of few layers of neurons.
Neurons in adjusting layers are connected with relative
quantitative weights. These weights are randomly cho-
sen, and they are changed through the training procedure,
so that the mean of the sum-of-squares error (MSE) is
minimized. The MSE is the squared difference between
the network outpu t and network target, averaged over all
of the cases [16,17]. Figure 1 shows the architecture of
the trained ANN model and Figure 2 is the neural net-
work development flow chart. Our trained ANN model
in our investigation was composed of four layers: 1) one
input layer with three inputs; 2) hidden layer with 6
neurons and 18 connections; 3) hidden layer with 2 neu-
rons and 12 connections 3. One output layer with one
outcome and 2 connections. The back propagation (BPN)
network algorithm is used as the learning algorithm to
train the artificial neural network [9,10].
Three inputs were BPD, AC, and AD. Furthermore, to
examine the performance of ANN model we calculate
mean absolute error (AE) and mean absolute percent
error (APE) between actual fetal weight and estimated
fetal weight.
4. RESULT
The median birth weight of twin A was 2390 g (150-
3200 g), and of twin B 2165 g (210-3250 g) in the vali-
dation group, respectiv ely. In validation group, the mean
absolute error (AE) and the mean absolute percent error
(APE) between estimated fetal weight and actual fetal
weight was 261.77 g and 7.81%. Also, the moderation
significant correlations between the actual fetal weight
and the estimated fetal weight of the validation group are
shown in Figure 3 (r = 0.9348, n = 121). Also, th e over-
all, high correlation between AC, AD, BPD and twin`s
EFW were 0.81, 0.87 and 0.84, respectively, which
shows the important effect of these parameters on twin`s
weight. It seems that the prediction error is known to
increase with increasing twin’s weight.
Figure 1. Architecture of the trained ANN model, BPD = bipa-
rietal distance, AC = abdominal circumference, AD= abdomi-
al diameter. n

H. Mohammadi et al. / J. Biomedical Science and Engineering 4 (2011) 46-50
48
Figure 2. The neural network development flow chart.
5. CONCLUSIONS
A neural network is a model that simulates the functions
of biologic neurons. The ability of a single neuron could
be greatly improved by connecting multiple neurons in
layers. Artificial neural networks are powerful non-l i nea r
models used vastly for classifying different types of data.
A neural network is composed of few layers of neurons.
Neurons in adjusting layers are connected with relative
quantitative weights. These weights are randomly cho-
sen, and then are changed through the training procedure,
so that the mean of the sum-of-squares error (MSE) is
minimized [2,11,12]. The ANNs learning algorithms can
be divided into two main groups that are supervised (or
Associative learning) and unsupervised (Self-Organiza-
tion) learning [2,9,11,14]. One of the most commonly
used supervised ANN model is back propagation (BPN)
network that uses back propagation learning algorithm [9-
11]. Back propagation algorithm is one of the well-known
Figure 3. Scattergram of the estimated fetal body weight vs.
the actual fetal body weight the by the ANN model in the vali-
dation group.
JBiSE

H. Mohammadi et al. / J. Biomedical Science and Engineering 4 (2011) 46-50
49
algorithms in neural networks. The back propagation
neural network is essentially a network of simple proc-
essing elements working together to produce a complex
output [11]. These elements or nodes are arranged into
different layers: input, middle and output. The output
from a back propagation neural network is computed
using a procedure known as the forward pass [2,8-11,15]:
1) The input layer propagates a particular input vector’s
components to each node in the middle layer. 2) Middle
layer nodes compute output values, which become inputs
to the nodes of the output layer. 3) The output layer
nodes compute the network output for the particular in-
put vector. The forward pass produces an output vector
for a given input vector based on the current state of the
network weights. Since the network weights are initial-
ized to random values, it is u nlikely that reasonable out-
puts will result before training . The weights are adjusted
to reduce the error by propagating the output error
backward through the network [2,9,15]. In this study, we
used the BPN algorithm to develop the ANN and prove
our hypothesis that BPD, AC and AD wi t hin A NN model
could reduce errors between estimated fetal weight and
actual fetal weight. The subjects in our series were a
group of women with healthy singleton fetus with
documentation of US examination with, BPD, AC and
AD. Some may wonder at our choice of the three input
parameters, thinking that they are not well justified. The
three dimensional variables are reasonable because of
the previous literature [9]. Also, the overall, high corre-
lation between AC, AD, BPD and twin’s EFW were 0.81,
0.87 and 0.84, respectively, which shows the important
effect of these parameters on twin’s weight. In our study,
the definition of an anomaly was for any fetus with a
major structural anomaly that could be diagnosed prena-
tally, such as holoprosencephaly, omphalocele, cystic
hygroma, etc. These were excluded from the study. We
might include some fetuses with rare and nonstructural
anomalies that could only be diagnosed postnatally by
genetic screening or metabolic methods, in which the
prenatal ultrasonic examination cannot demonstrate any
structural abnormality. However, we believe that this
point makes only little impact on the stud y because these
nonstructural anomalies are too rare [9].
In our study, the mean absolute error (AE) and the
mean absolute percent error (APE) between estimated
fetal weight and actual fetal weight were 162.71 g and
7.81%, respectively. The fetuses in weight range of
(>2500 g) are the lowest accurate fetal weight estimation
in validation group (AE = 269 g, APE= 10.51%), we
think that, as the fetus grows are more quick at the last
trimester and we considered babies within 3 days of de-
livery, it might be one part of the error in this weight
range is related to fetus grows within this estimation of
fetal weight. In th is ANN model we have 4 layers; input
layer, two median layers and output layer. We have three
input variables AC, AD and BPD. In all cases the esti-
mated date of confinement had been established by ul-
trasound scan at 20 weeks of gestation. Median maternal
age was 26.7 years (range 15-44), median number of
pregnancies was 2 (1-5), number of previous deliveries 0
(0-3), and median gestational age at delivery was 36
weeks (14-41). The median birth weight of twin A was
2390 g (160-29 18 g) , an d of tw in B 2 265 g (210 -2868 g)
in the training group. Also, the median birth weight of
twin A was 2190 g (150-3200 g), and of twin B 2165 g
(210-2868 g) in the validation group.
Also estimation of fetal weight by ANN model at the
weight range of (<1500 g) are the most accurate result. It
seems that the prediction birth weight error is known to
increase with increasing weight of twins, week by week.
In conclusion, our study demonstrates that our single
multiplicative neuron model is a well-established model
and can be used to estimate fetal weight. However, more
accuracy of fetal weight estimation is in need of further
studies.
6. ACKNOWLEDGEMENTS
This study was supported in part by grants from university of science
and research branch Islamic Azad University, Tehran. The authors are
grateful to all doctors for the ultrasound measurements; Ms. Fatemeh
Nematollahi and Ms. Fatemeh Bani and their assistance; and Ali
Ghafari at the Department of Obstetrics and gynecology, Madaran
Medical Faculty for equipment supply.
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