Ultrasound estimation of fetal weight in twins by artificial neural network

Mohammadi, Hânieh; Nemati, M; Allahmoradi, Zohreh; Raissi, Hoda Forghani; Esmaili, Somayeh Saraf; Sheikhani, Ali

doi:10.4236/jbise.2011.41006

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…Cheng et al [ 9 ] proposed a clustering-based ANN model for birthweight prediction. Mohammadi et al [ 10 ] used an ANN to predict the weight of twin fetuses. Feng M [ 11 ] used an SVM and deep belief network (DBN) ML solution to improve the fetal weight estimation accuracy and to help the clinicians identify potential risks before delivery.…”

Section: Introductionmentioning

confidence: 99%

Fetal birthweight prediction with measured data by a temporal machine learning method

Tao

Yuan

Sun

et al. 2021

BMC Med Inform Decis Mak

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Background Birthweight is an important indicator during the fetal development process to protect the maternal and infant safety. However, birthweight is difficult to be directly measured, and is usually roughly estimated by the empirical formulas according to the experience of the doctors in clinical practice. Methods This study attempts to combine multiple electronic medical records with the B-ultrasonic examination of pregnant women to construct a hybrid birth weight predicting classifier based on long short-term memory (LSTM) networks. The clinical data were collected from 5,759 Chinese pregnant women who have given birth, with more than 57,000 obstetric electronic medical records. We evaluated the prediction by the mean relative error (MRE) and the accuracy rate of different machine learning classifiers at different predicting periods for first delivery and multiple deliveries. Additionally, we evaluated the classification accuracies of different classifiers respectively for the Small-for-Gestational-age (SGA), Large-for-Gestational-Age (LGA) and Appropriate-for-Gestational-Age (AGA) groups. Results The results show that the accuracy rate of the prediction model using Convolutional Neuron Networks (CNN), Random Forest (RF), Linear-Regression, Support Vector Regression (SVR), Back Propagation Neural Network(BPNN), and the proposed hybrid-LSTM at the 40th pregnancy week for first delivery were 0.498, 0.662, 0.670, 0.680, 0.705 and 0.793, respectively. Among the groups of less than 39th pregnancy week, the 39th pregnancy week and more than 40th week, the hybrid-LSTM model obtained the best accuracy and almost the least MRE compared with those of machine learning models. Not surprisingly, all the machine learning models performed better than the empirical formula. In the SGA, LGA and AGA group experiments, the average accuracy by the empirical formula, logistic regression (LR), BPNN, CNN, RF and Hybrid-LSTM were 0.780, 0.855, 0.890, 0.906, 0.916 and 0.933, respectively. Conclusions The results of this study are helpful for the birthweight prediction and development of guidelines for clinical delivery treatments. It is also useful for the implementation of a decision support system using the temporal machine learning prediction model, as it can assist the clinicians to make correct decisions during the obstetric examinations and remind pregnant women to manage their weight.

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Section: Introductionmentioning

confidence: 99%

Fetal birthweight prediction with measured data by a temporal machine learning method

Tao

Yuan

Sun

et al. 2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Recently, artificial neural networks (ANN) has been applied by many researches to predict fetal weight to overcome the problems of traditional regression methods [14], [15], [16]. Comparison of estimated fetal weight (EFW) accuracy showed that these ANN models both significantly outperformed the commonly-used regression formulas.…”

Section: Introductionmentioning

confidence: 99%

Fetal Weight Estimation via Ultrasound Using Machine Learning

Feng

Wan

et al. 2019

IEEE Access

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Accurate fetal weight estimation is important for both fetuses and their mothers. The low birth weight (LBW, birth weight < 2500 g) and high birth weight (HBW, birth weight ≥ 4000 g) fetuses and their mothers are linked to both short and long-term health outcomes such as high perinatal mortality rate, various complications, and chronic disease in life. Because of the imbalanced small data sets and body size heterogeneities between different fetal weight groups, it is difficult for the commonly used regression formulas to get a satisfying performance, especially for the HBW and LBW fetuses. The aim of this paper is to propose a machine learning solution to improve fetal weight estimation accuracy and to help the clinicians identify potential risks before delivery. A clinical data set of 7875 singleton fetuses were analyzed. The synthetic minority over-sampling technique (SMOTE) was employed to solve the imbalanced learning problem. Then, the support vector machine (SVM) algorithm was utilized for fetal weight classification. Finally, the deep belief network (DBN) was employed to estimate the fetal weight based on different ultrasound parameters. The estimation result of the proposed model showed a mean absolute percent error (MAPE) of 6.09 ± 5.06% and mean absolute error (MAE) of 198.55±158.63g. It demonstrated that our model outperformed the commonly-used regression formulas, especially for the HBW and LBW fetuses.INDEX TERMS Deep belief network, fetal weight estimation, synthetic minority over-sampling technique, support vector machine, ultrasound.

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“…ANNs are the computerized analog of a biological neural system and they are the important class of pattern recognizer which are useful for a wide variety of applications [14]. The architecture of the ANN model is to develop relationships between the input and output data through training on a data set [15,16]. ANNs have attracted the interest of many researches in the field of chemistry as modeling tools for multivariate calibrations [4,13,[15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The architecture of the ANN model is to develop relationships between the input and output data through training on a data set [15,16]. ANNs have attracted the interest of many researches in the field of chemistry as modeling tools for multivariate calibrations [4,13,[15][16][17][18][19]. Among neural networks, the most popular is the backpropagation neural network (BPNN) [4,13,17,[20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%