Cluster-Based Artificial Neural Network on Ultrasonographic Parameters for Fetal Weight

Estimation

Reporter ： Huang Kun-Yi

From ： International Federation for Medical and Biological Engineering.BY Yueh-Chin Cheng, Chi-Chun Hsia, Fong-Ming Chang, Chun-Ju Hou, Yu-Hsien Chiu, and Kao-Chi Chung.

Outline Introduction Material and Methods Experiments and Results Discussion

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Introduction Accurate estimation of fetal weight

(EFW) and fetal growth rate become an important is in obstetrics.

In 2008, fetal birth weight.[1] Low birth weight (less than 2.5 Kg) ：

8.54% Macrosomia (equal to or more than 4 Kg) ：

1.87% Low birth weight infants have high risk

incidences of cerebral dysfunction.

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Introduction Based on ultrasonographic parameters

(USPs), fetal weight estimation methods： Multiple regression models. Artificial neural network models. (ANN)

Large estimation error is a thorny problem in the clinical treatment for obstetricians.

The accuracy of fetal weight estimated is eagerly waiting to be improved.

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Introduction This study proposes a cluster-based

ANN model to estimate fetal weight for different body figure.

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System Diagram6

Material and Method7

Fetal biometric measurements were quantified by ultrasound with a 3.5 MHz convex transducer. Numerical parameters ： 7 Nominal parameters ： 2

Material and Method8

Parameter Abbreviation Chinese

Biparietal diameter BPD 頂骨直徑Occipitofrontal

diameterOFD 額頭直徑

Abdominal circumference

AC 腹圍Head circumference HC 頭圍

Femur length FL 股骨長度Gestational age GA 胎齡

Birth weight BW 出生重量Gender SEX 性別

Fetal presentation FP 胎兒介紹

Material and Method9

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U is the total numbers of USPs.

F is the total numbers of fetal.

Material and Method10

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Use Singular value decomposition. (SVD)

K-means Method for Fetal Size Classification.

Material and Method11

Cluster-Based ANN Modeling.

Experiments and Results Estimated fetal weights and the birth

weights. Mean absolute error(MAE). Mean absolute percent error(MAPE).

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Experiments and Results13

Cluster Train Data Test Data MAE MAPE

All 1489 638 149.4±110.2g

4.9±3.5%

I 95 40 104.5±93.6g

5.4±4.7%

II 743 319 147.1±108.4g

4.9±3.6%

III 617 264 166.2±111.2g

4.8±3.2%

IV 34 15 19.8±19.2g 2.9±2.5%

Experiments and Results14

Discussion ANN mode is trained predicting fetal

weight for each body figure cluster based on BPN algorithm and has also verified that the accuracy of fetal weight estimation of the cluster-based ANN model is genuinely preferable than those previous models.

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Thank you for your attend~

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